Pub Date : 2023-04-30DOI: 10.24843/jim.2023.v11.i01.p05
Putu Alan Arismandika, Kadek Yota, Ernanda Aryanto, I. Made, Gede Sunarya
Fast problem-solving is one of the main keys to a company's reputation. Currently, most of the company's business processes are accommodated by applications. Often, applications in companies experience problems due to internal and external factors. Users send a request to solve problems by submitting the problem to the helpdesk tool or application. The requests at the helpdesk tool or application do not go directly to the technician who has the authority to solve them but instead go to the operator first and then escalate to the technician to be resolved. This process affects the efficiency of problem-solving time. This study proposes the use of text classification with deep learning to complete operator work. The method proposed in this study is the BiLSTM method. The total data used in this research is 160,000 helpdesk request data by dividing the data by 128,000 resolved data as training data and 32,000 on-progress data as testing data or 80% training data and 20% testing data. The research was conducted using 13 labels for the technician assignment route process Performance measurement of this study using a confusion matrix which obtained an accuracy 91.18%, precision 95.05%, and recall 93.28%
{"title":"Helpdesk Ticket Classification for Technician Assignment Routes Using BiLSTM","authors":"Putu Alan Arismandika, Kadek Yota, Ernanda Aryanto, I. Made, Gede Sunarya","doi":"10.24843/jim.2023.v11.i01.p05","DOIUrl":"https://doi.org/10.24843/jim.2023.v11.i01.p05","url":null,"abstract":"Fast problem-solving is one of the main keys to a company's reputation. Currently, most of the company's business processes are accommodated by applications. Often, applications in companies experience problems due to internal and external factors. Users send a request to solve problems by submitting the problem to the helpdesk tool or application. The requests at the helpdesk tool or application do not go directly to the technician who has the authority to solve them but instead go to the operator first and then escalate to the technician to be resolved. This process affects the efficiency of problem-solving time. This study proposes the use of text classification with deep learning to complete operator work. The method proposed in this study is the BiLSTM method. The total data used in this research is 160,000 helpdesk request data by dividing the data by 128,000 resolved data as training data and 32,000 on-progress data as testing data or 80% training data and 20% testing data. The research was conducted using 13 labels for the technician assignment route process Performance measurement of this study using a confusion matrix which obtained an accuracy 91.18%, precision 95.05%, and recall 93.28%","PeriodicalId":32334,"journal":{"name":"Jurnal Ilmiah Merpati Menara Penelitian Akademika Teknologi Informasi","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41911022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-26DOI: 10.24843/jim.2023.v11.i01.p02
Mira Nabila, F. Alzami, Rama Aria Megantara, Fikri Firdaus Tananto, Hasan Aminda Syafrudin, L. Handoko, Chaerul Umam
Red Onions are a horticultural commodity belonging to the spice vegetable group and an important role for economy of the Indonesian people. In red onion farming there have a problem of price fluctuations which result in an uneven and less transparent distribution of red onions yields, thus affecting both consumers and producers. To answer these problems, we designed a system to maintain and store red onion harvest data for farmers, collectors, distributors, and retailers in the form of a blockchain-based supply chain system. This system can maintain the validity of transactions in the supply chain of red onion farming with a private blockchain with Hyperledger Fabric. Then the data on the blockchain system will be displayed through the Hyperledger Explorer website. This system already passed the Black Box Testing system. From the research and testing of the system that has been made, this system can help the red onion farming to maintain the validity of transactions in the supply chain management.
{"title":"Implementation of a Supply chain Management System Blockchain-Based in Red Onion Farming","authors":"Mira Nabila, F. Alzami, Rama Aria Megantara, Fikri Firdaus Tananto, Hasan Aminda Syafrudin, L. Handoko, Chaerul Umam","doi":"10.24843/jim.2023.v11.i01.p02","DOIUrl":"https://doi.org/10.24843/jim.2023.v11.i01.p02","url":null,"abstract":"Red Onions are a horticultural commodity belonging to the spice vegetable group and an important role for economy of the Indonesian people. In red onion farming there have a problem of price fluctuations which result in an uneven and less transparent distribution of red onions yields, thus affecting both consumers and producers. To answer these problems, we designed a system to maintain and store red onion harvest data for farmers, collectors, distributors, and retailers in the form of a blockchain-based supply chain system. This system can maintain the validity of transactions in the supply chain of red onion farming with a private blockchain with Hyperledger Fabric. Then the data on the blockchain system will be displayed through the Hyperledger Explorer website. This system already passed the Black Box Testing system. From the research and testing of the system that has been made, this system can help the red onion farming to maintain the validity of transactions in the supply chain management.","PeriodicalId":32334,"journal":{"name":"Jurnal Ilmiah Merpati Menara Penelitian Akademika Teknologi Informasi","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41933937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-26DOI: 10.24843/jim.2023.v11.i01.p01
Kadek Noppi, A. Jaya, Fauzi, Agus Suryana, Albertus Dwiyoga Widiantoro, Dewa Kadek, Laksana Digita
Nusantara, Indonesia's new capital, has a land area 256,142 ha and a marine area 68,189 ha. Transmigrant residents in the North Penajam Paser Regency region has increased as a result. The difficulty of finding a place of worship in the closest location because they are unfamiliar with their location or the nearby roadways. We can utilize data visualization to show the distribution of places of worship in order to solve this issue. Analysis was done on several types of houses of worship, including mosques, churches, and musholla. Python is the programming language, and the libraries used are NumPy, Pandas, Matplotlib, Seaborn, RegEx, and Folium. Muslims for the majority of places of worship in IKN Nusantara (91.26%), musholla making up 57.92% of the total and mosques making up 33.33% and 8.74% are owned by Christians. Sukaraja Village, Semoi Dua Village, Argo Mulyo Village, and Tengin Baru Village have the highest concentration. Keywords: Data Visualization, House of Worship, IKN Nusantara, Python
{"title":"Data Visualization Of House Of Worship Distribution In The IKN Nusantara Region Using Python","authors":"Kadek Noppi, A. Jaya, Fauzi, Agus Suryana, Albertus Dwiyoga Widiantoro, Dewa Kadek, Laksana Digita","doi":"10.24843/jim.2023.v11.i01.p01","DOIUrl":"https://doi.org/10.24843/jim.2023.v11.i01.p01","url":null,"abstract":"Nusantara, Indonesia's new capital, has a land area 256,142 ha and a marine area 68,189 ha. Transmigrant residents in the North Penajam Paser Regency region has increased as a result. The difficulty of finding a place of worship in the closest location because they are unfamiliar with their location or the nearby roadways. We can utilize data visualization to show the distribution of places of worship in order to solve this issue. Analysis was done on several types of houses of worship, including mosques, churches, and musholla. Python is the programming language, and the libraries used are NumPy, Pandas, Matplotlib, Seaborn, RegEx, and Folium. Muslims for the majority of places of worship in IKN Nusantara (91.26%), musholla making up 57.92% of the total and mosques making up 33.33% and 8.74% are owned by Christians. Sukaraja Village, Semoi Dua Village, Argo Mulyo Village, and Tengin Baru Village have the highest concentration. Keywords: Data Visualization, House of Worship, IKN Nusantara, Python","PeriodicalId":32334,"journal":{"name":"Jurnal Ilmiah Merpati Menara Penelitian Akademika Teknologi Informasi","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43213993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-26DOI: 10.24843/jim.2023.v11.i01.p03
I. Gede, Nyoman Agung Jayarana, Kadek Yota, Ernanda Aryanto, I. Made, Gede Sunarya
Udayana University has developed numerous information systems to accommodate all types of academic and non-academic activities. Because the process of retrieving data from other systems still employs the traditional method with a joint scheme between databases, the resulting data quality becomes increasingly inaccurate over time. Some systems have also established simple web services that do not adhere to the Restful API authoring rules. Based on these issues, modifications were made to the data integration process using Microservices Management, Service Oriented Architecture, and OAuth 2.0 as the security protocol. The results indicate that the microservices management system is highly effective and efficient for improving process performance, has a low risk level, a good control test validation of 93,33 %, and a usability grade of B, indicating that the system is deemed good and usable.
{"title":"Development of Service-Oriented Architecture-Based Microservices Management as a Data Integration Service (Case Study: Udayana University)","authors":"I. Gede, Nyoman Agung Jayarana, Kadek Yota, Ernanda Aryanto, I. Made, Gede Sunarya","doi":"10.24843/jim.2023.v11.i01.p03","DOIUrl":"https://doi.org/10.24843/jim.2023.v11.i01.p03","url":null,"abstract":"Udayana University has developed numerous information systems to accommodate all types of academic and non-academic activities. Because the process of retrieving data from other systems still employs the traditional method with a joint scheme between databases, the resulting data quality becomes increasingly inaccurate over time. Some systems have also established simple web services that do not adhere to the Restful API authoring rules. Based on these issues, modifications were made to the data integration process using Microservices Management, Service Oriented Architecture, and OAuth 2.0 as the security protocol. The results indicate that the microservices management system is highly effective and efficient for improving process performance, has a low risk level, a good control test validation of 93,33 %, and a usability grade of B, indicating that the system is deemed good and usable.","PeriodicalId":32334,"journal":{"name":"Jurnal Ilmiah Merpati Menara Penelitian Akademika Teknologi Informasi","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41418671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-26DOI: 10.24843/jim.2023.v11.i01.p04
Irianto Yohanes Sampe, Putu Agus, E. Pratama, Ni Made, Ika Marini Mandenni
The application of integrated information systems in business requires improvements related to system performance. A business can run well if every division in it is integrated with one another. One of the obstacles in this research is that the sales business process is still done manually, data storage on Ms. Excel, and between divisions one with other divisions have not been integrated. The technology proposed as a solution to these problems is Enterprise Resource Planning. This research was conducted by implementing the ERP model using WebERP which is used to assist the entire series of business processes at Fushia Stores. The research conducted focuses on three parts of the process, namely on the Sales, Purchase, and Inventory sections. The implemented implementation has met the needs of Fushia Stores with a system trial result of 71.66% or the equivalent of an agree index scale. The system implemented is able to improve and integrate all data so that data processing becomes more effective and efficient to use. Keywords: Business Process, Enterprise Resource Planning, Fushia Shop, Likert Scala, WebERP.
{"title":"Implementation Enterprise Resource Planning Sales and Purchase of Goods Using WebERP Fushia Clothing Store Denpasar","authors":"Irianto Yohanes Sampe, Putu Agus, E. Pratama, Ni Made, Ika Marini Mandenni","doi":"10.24843/jim.2023.v11.i01.p04","DOIUrl":"https://doi.org/10.24843/jim.2023.v11.i01.p04","url":null,"abstract":"The application of integrated information systems in business requires improvements related to system performance. A business can run well if every division in it is integrated with one another. One of the obstacles in this research is that the sales business process is still done manually, data storage on Ms. Excel, and between divisions one with other divisions have not been integrated. The technology proposed as a solution to these problems is Enterprise Resource Planning. This research was conducted by implementing the ERP model using WebERP which is used to assist the entire series of business processes at Fushia Stores. The research conducted focuses on three parts of the process, namely on the Sales, Purchase, and Inventory sections. The implemented implementation has met the needs of Fushia Stores with a system trial result of 71.66% or the equivalent of an agree index scale. The system implemented is able to improve and integrate all data so that data processing becomes more effective and efficient to use. \u0000Keywords: Business Process, Enterprise Resource Planning, Fushia Shop, Likert Scala, WebERP.","PeriodicalId":32334,"journal":{"name":"Jurnal Ilmiah Merpati Menara Penelitian Akademika Teknologi Informasi","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47563542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-30DOI: 10.24843/jim.2022.v10.i03.p06
Yonas Prima Arga Rumbyarso, Nuke L. Chusna, A. Khumaidi
Evaluation of Ciliwung river water quality can be done by analyzing the distribution of dissolved oxygen (DO). The purpose of this research is to analyze the environmental parameters that affect the distribution of DO, by carrying out predictive modeling to estimate the distribution of DO in the Ciliwung River. The research data used primary data and secondary data, some of which were obtained from previous studies. The water quality parameters used are DO, temperature, biochemical oxygen demand, chemical oxygen demand, power of hydrogen, and turbidity. The dataset used has a missing value of 28.8%. To optimize the model results, preprocessing is carried out using a machine learning approach, namely comparing support vector machine (SVM), artificial neural networks (ANN), and linear regression. The three models were compared to predict DO, the results of performance evaluation of the SVM, ANN and Streeter-Phelps models had RMSE values of 0.110, 0.771, and 0.114.
{"title":"Dissolved Oxygen Prediction of the Ciliwung River using Artificial Neural Networks, Support Vector Machine, and Streeter-Phelps","authors":"Yonas Prima Arga Rumbyarso, Nuke L. Chusna, A. Khumaidi","doi":"10.24843/jim.2022.v10.i03.p06","DOIUrl":"https://doi.org/10.24843/jim.2022.v10.i03.p06","url":null,"abstract":"Evaluation of Ciliwung river water quality can be done by analyzing the distribution of dissolved oxygen (DO). The purpose of this research is to analyze the environmental parameters that affect the distribution of DO, by carrying out predictive modeling to estimate the distribution of DO in the Ciliwung River. The research data used primary data and secondary data, some of which were obtained from previous studies. The water quality parameters used are DO, temperature, biochemical oxygen demand, chemical oxygen demand, power of hydrogen, and turbidity. The dataset used has a missing value of 28.8%. To optimize the model results, preprocessing is carried out using a machine learning approach, namely comparing support vector machine (SVM), artificial neural networks (ANN), and linear regression. The three models were compared to predict DO, the results of performance evaluation of the SVM, ANN and Streeter-Phelps models had RMSE values of 0.110, 0.771, and 0.114.","PeriodicalId":32334,"journal":{"name":"Jurnal Ilmiah Merpati Menara Penelitian Akademika Teknologi Informasi","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44652549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-30DOI: 10.24843/jim.2022.v10.i03.p05
F. Alzami, Fikri Diva Sambasri, Rifqi Mulya Kiswanto, Rama Aria Megantara, Ahmad Akrom, R. A. Pramunendar, D. P. Prabowo, Puri Sulistiyawati
E-commerce is the activity of selling and buying goods through an online system or online. One of the business models in which consumers sell products to other consumers is the Customer to Customer (C2C) business model. One of the things that need to be considered in this business model is knowing the level of customer loyalty. By knowing the level of customer loyalty, the company can provide several different treatments to its customers so that they can maintain good relations with customers and can increase product purchase revenue. In this study, the author wants to segment customers on data in E-commerce companies in Brazil using the K-Means clustering algorithm using the RFM (Recency, Frequency, Monetary) feature. There are also several ETL stages of research that must be carried out, namely taking data from the open public data site (Kaggle), which consist of more than 9 tables (extract), then merging the data to select some data that needs to be used (transform and load), understanding data by displaying it in graphic form, conducting data selection to select features / attributes. which is in accordance with the proposed method, performs data preprocessing, and creates a model to get the cluster. Based on the results of the research that has been done, the number of clusters is 4 clusters with the evaluation value of the model using the silhouette score is 0.470.
{"title":"Implementation Of ETL E-Commerce For Customer Clustering Using RFM And K-Means Clustering","authors":"F. Alzami, Fikri Diva Sambasri, Rifqi Mulya Kiswanto, Rama Aria Megantara, Ahmad Akrom, R. A. Pramunendar, D. P. Prabowo, Puri Sulistiyawati","doi":"10.24843/jim.2022.v10.i03.p05","DOIUrl":"https://doi.org/10.24843/jim.2022.v10.i03.p05","url":null,"abstract":"E-commerce is the activity of selling and buying goods through an online system or online. One of the business models in which consumers sell products to other consumers is the Customer to Customer (C2C) business model. One of the things that need to be considered in this business model is knowing the level of customer loyalty. By knowing the level of customer loyalty, the company can provide several different treatments to its customers so that they can maintain good relations with customers and can increase product purchase revenue. In this study, the author wants to segment customers on data in E-commerce companies in Brazil using the K-Means clustering algorithm using the RFM (Recency, Frequency, Monetary) feature. There are also several ETL stages of research that must be carried out, namely taking data from the open public data site (Kaggle), which consist of more than 9 tables (extract), then merging the data to select some data that needs to be used (transform and load), understanding data by displaying it in graphic form, conducting data selection to select features / attributes. which is in accordance with the proposed method, performs data preprocessing, and creates a model to get the cluster. Based on the results of the research that has been done, the number of clusters is 4 clusters with the evaluation value of the model using the silhouette score is 0.470.","PeriodicalId":32334,"journal":{"name":"Jurnal Ilmiah Merpati Menara Penelitian Akademika Teknologi Informasi","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45159580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-28DOI: 10.24843/jim.2022.v10.i03.p03
Harry Dwiyana Kartika, Getah Ester Hayatulah, A. Khumaidi
Proses persidangan suatu perkara pidana di Pengadilan Negeri Bekasi pada tahun 2019-2021 dengan rata-rata lama proses yang diperlukan untuk memutuskan perkara oleh hakim adalah 65-an hari. Pada penelitian ini mengusulkan penggunaan machine learning sebagai alat bantu untuk mempercepat keputusan hakim. Kasus tindak pidana berdasarkan jenis acara pidana dibagi menjadi 3 jenis yaitu pidana biasa, pidana singkat, dan pidana cepat. Data penelitian yang digunakan adalah jenis acara pidana biasa dengan status perkara minutasi yang dipublikasikan sebanyak 1.642 kasus. Proses pengolahan data mengunakan python dengan preprocessing data case folding, remove punctuation, tokenization dan removal stopword kemudian untuk pembobotan kata menggunakan TF-IDF. Untuk memprediksi putusan lama pemidanaan menggunakan pendekatan klasifikasi Support Vector Machine. Sebelum pemodelan dilakukan splitting data dengan perbandingan 80:20 dan hasil perbandingan pemodelan klasifikasi menggunakan SVM dengan 4 kernel yaitu linear (89,4%), RBF (88,4%), sigmoid (88,4%), dan polynomial (89,1%). Kernel SVM terbaik adalah kernel linear dengan nilai akurasi sebesar 89,4% dan nilai error sebesar 10,6%.
{"title":"Comparison of Kernel Support Vector Machine in Predicting Judges' Decisions at the Bekasi District Court","authors":"Harry Dwiyana Kartika, Getah Ester Hayatulah, A. Khumaidi","doi":"10.24843/jim.2022.v10.i03.p03","DOIUrl":"https://doi.org/10.24843/jim.2022.v10.i03.p03","url":null,"abstract":"Proses persidangan suatu perkara pidana di Pengadilan Negeri Bekasi pada tahun 2019-2021 dengan rata-rata lama proses yang diperlukan untuk memutuskan perkara oleh hakim adalah 65-an hari. Pada penelitian ini mengusulkan penggunaan machine learning sebagai alat bantu untuk mempercepat keputusan hakim. Kasus tindak pidana berdasarkan jenis acara pidana dibagi menjadi 3 jenis yaitu pidana biasa, pidana singkat, dan pidana cepat. Data penelitian yang digunakan adalah jenis acara pidana biasa dengan status perkara minutasi yang dipublikasikan sebanyak 1.642 kasus. Proses pengolahan data mengunakan python dengan preprocessing data case folding, remove punctuation, tokenization dan removal stopword kemudian untuk pembobotan kata menggunakan TF-IDF. Untuk memprediksi putusan lama pemidanaan menggunakan pendekatan klasifikasi Support Vector Machine. Sebelum pemodelan dilakukan splitting data dengan perbandingan 80:20 dan hasil perbandingan pemodelan klasifikasi menggunakan SVM dengan 4 kernel yaitu linear (89,4%), RBF (88,4%), sigmoid (88,4%), dan polynomial (89,1%). Kernel SVM terbaik adalah kernel linear dengan nilai akurasi sebesar 89,4% dan nilai error sebesar 10,6%.","PeriodicalId":32334,"journal":{"name":"Jurnal Ilmiah Merpati Menara Penelitian Akademika Teknologi Informasi","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46498645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-28DOI: 10.24843/jim.2022.v10.i03.p04
Reyhan Todo, Noer Yamin, Made Agus, D. Suarjaya, Putu Agus, E. Pratama
SISAKTI application is an information system to facilitate online administration of Udayana University student participation credit units. Until now, there has been no security testing carried out on the SISAKTI application, therefore this study aimed to test the security of SISAKTI application using Black Box penetration testing technique, conduct an assessment of system vulnerabilities and provide recommendations for improvements. The method used is by following the guidelines from OWASP Testing Guide version 4 using Information Gathering, Input Validation Testing, and Authorization Testing modules. From these three modules, there were 28 sub-tests that were successfully carried out, the results were 15 positive tests, 6 negative tests, and 7 tests which cannot be done, from the 28 sub-tests there are 8 vulnerabilities that have a direct effect on the system and are assessed using CVSS calculator, the results are 6 vulnerabilities have a vulnerable value from 6.4 (Medium) to 9.9 (Critical).
{"title":"Penetration Testing on the SISAKTI Application at Udayana University Using the OWASP Testing Guide Version 4","authors":"Reyhan Todo, Noer Yamin, Made Agus, D. Suarjaya, Putu Agus, E. Pratama","doi":"10.24843/jim.2022.v10.i03.p04","DOIUrl":"https://doi.org/10.24843/jim.2022.v10.i03.p04","url":null,"abstract":"SISAKTI application is an information system to facilitate online administration of Udayana University student participation credit units. Until now, there has been no security testing carried out on the SISAKTI application, therefore this study aimed to test the security of SISAKTI application using Black Box penetration testing technique, conduct an assessment of system vulnerabilities and provide recommendations for improvements. The method used is by following the guidelines from OWASP Testing Guide version 4 using Information Gathering, Input Validation Testing, and Authorization Testing modules. From these three modules, there were 28 sub-tests that were successfully carried out, the results were 15 positive tests, 6 negative tests, and 7 tests which cannot be done, from the 28 sub-tests there are 8 vulnerabilities that have a direct effect on the system and are assessed using CVSS calculator, the results are 6 vulnerabilities have a vulnerable value from 6.4 (Medium) to 9.9 (Critical).","PeriodicalId":32334,"journal":{"name":"Jurnal Ilmiah Merpati Menara Penelitian Akademika Teknologi Informasi","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46282132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-28DOI: 10.24843/jim.2022.v10.i03.p02
Ni Putu Sutramiani, I. W. A. S. Darma, D. M. S. Arsa
This paper presents a simple approach to the handwritten Balinese script characters recognition in palm-leaf lontar manuscripts. The Lontar manuscript is one of the cultural heritages found in Bali. Lontar manuscripts are written using a pengrupak, which is a kind of knife for writing on palm leaves. To give color to the results of the writing, candlenut is used so that the writing appears clear. In this paper, we apply the projection profile at the segmentation stage to get the handwritten Balinese script characters in the lontar manuscript. The palm leaf manuscript that we use is the Wariga Palalubangan palm leaf. The recognition process is carried out by implementing K-Nearest Neighbor in the recognition process. The recognition was made on the Wianjana script obtained from lontar manuscripts using 720 images consisting of 18 classes as dataset training. The test results showed that the level of recognition accuracy was obtained by 52% in the characters of handwritten Balinese scripts derived from lontar manuscripts and 92% in the characters of handwritten Balinese scripts on paper.
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