This study aims to analyze the factors of student satisfaction with the services of educational institutions during the Covid-19 pandemic. In December 2019, the issue of the corona virus spread in the world. Until March 2020, the issue of the corona virus reached Indonesia. Several new policies emerged from the Indonesian government based on the development of the corona virus issue, one of which was the service activities of educational institutions which were carried out face-to-face and then carried out non-face to face. Educational institutions are one of the institutions affected by this policy. The object of this research is active students in Indonesia. Data collection was carried out randomly by distributing online questionnaires through a structured evaluation form consisting of several questions to assess non-face-to-face services in educational institutions. Service quality (SERVQUAL) and interest performance analysis (IPA) are used as the methods in this study. Data analysis was performed using SPSS software. There is a gap value of -0.650 from the results of the analysis of service quality in educational institutions. This means that active students in Indonesia are dissatisfied with non-face-to-face services at educational institutions.
{"title":"Analisis Kepuasan Mahasiswa Terhadap Pelayanan Institusi Secara Online Pada Masa Pandemi Covid-19","authors":"Kartinasari Ayuhikmatin Sekarjati, Andrean Emaputra, Bekti Utaminingsih","doi":"10.31289/jime.v7i1.9435","DOIUrl":"https://doi.org/10.31289/jime.v7i1.9435","url":null,"abstract":"This study aims to analyze the factors of student satisfaction with the services of educational institutions during the Covid-19 pandemic. In December 2019, the issue of the corona virus spread in the world. Until March 2020, the issue of the corona virus reached Indonesia. Several new policies emerged from the Indonesian government based on the development of the corona virus issue, one of which was the service activities of educational institutions which were carried out face-to-face and then carried out non-face to face. Educational institutions are one of the institutions affected by this policy. The object of this research is active students in Indonesia. Data collection was carried out randomly by distributing online questionnaires through a structured evaluation form consisting of several questions to assess non-face-to-face services in educational institutions. Service quality (SERVQUAL) and interest performance analysis (IPA) are used as the methods in this study. Data analysis was performed using SPSS software. There is a gap value of -0.650 from the results of the analysis of service quality in educational institutions. This means that active students in Indonesia are dissatisfied with non-face-to-face services at educational institutions.","PeriodicalId":479709,"journal":{"name":"JIME (Journal of Industrial and Manufacture Engineering)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135429250","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}
To become a partner of a core company on a broiler plasma farm, a farmer must have good performance. The performance measurement of plasma farms was carried out by the core company with a number called performance index (IP), where this IP reflects the value of the plasma farmers that will be used by the core company to determine the next cooperative relationship. The IP calculation is done at the end of a maintenance cycle by the field extension officer (FEO) based on the results of daily reports. In determining the IP calculation, the factors seen are The Feed Consumption Rate (FCR), Mortality, and the use of drugs, vitamins, and chemicals. For Plasma farmers, the obtained IP was only a report from the results of work in one maintenance cycle, while management in the core supply chain process was not known, how the farm's performance was. Prior research succeeded in designing an instrument for Assess Supply Chain Performance for Broiler Plasma Farms using five core processes SCOR and Analytical Hierarchy (AHP) for weighting. This research will measure the supply chain performance of several Plasma farms with the designed instrument. From the results of this performance measurement, plasma farmers will get an idea of which core processes need to be improved so that the performance in the next cycle will be better. In addition, this research will also look for the correlation between the performance obtained and the IP that has been assessed by the core company.
{"title":"Supply Chain Performance Measurement for One Maintenance Cycle of Broiler Plasma Farm","authors":"Elly Ismiyah","doi":"10.31289/jime.v7i1.9326","DOIUrl":"https://doi.org/10.31289/jime.v7i1.9326","url":null,"abstract":"To become a partner of a core company on a broiler plasma farm, a farmer must have good performance. The performance measurement of plasma farms was carried out by the core company with a number called performance index (IP), where this IP reflects the value of the plasma farmers that will be used by the core company to determine the next cooperative relationship. The IP calculation is done at the end of a maintenance cycle by the field extension officer (FEO) based on the results of daily reports. In determining the IP calculation, the factors seen are The Feed Consumption Rate (FCR), Mortality, and the use of drugs, vitamins, and chemicals. For Plasma farmers, the obtained IP was only a report from the results of work in one maintenance cycle, while management in the core supply chain process was not known, how the farm's performance was. Prior research succeeded in designing an instrument for Assess Supply Chain Performance for Broiler Plasma Farms using five core processes SCOR and Analytical Hierarchy (AHP) for weighting. This research will measure the supply chain performance of several Plasma farms with the designed instrument. From the results of this performance measurement, plasma farmers will get an idea of which core processes need to be improved so that the performance in the next cycle will be better. In addition, this research will also look for the correlation between the performance obtained and the IP that has been assessed by the core company.","PeriodicalId":479709,"journal":{"name":"JIME (Journal of Industrial and Manufacture Engineering)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135429246","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}
Mesin merupakan salah satu peranan penting dalam keberhasilan perusahaan dalam memenuhi permintaan konsumen. Desalination Plant 2 di PT Sari Dumai Sejati merupakan plant pengolahan air laut. Permasalahan yang terjadi adalah rendahnya efesiensi produksi air laut tersebut. Mesin produksi yang digunakan sering mengalami breakdown, breakdown diperoleh dari catridge filter, pompa DMF, pompa SWRO, dan pompa BWRO. Berdasarkan permasalahan tersebut maka dilakukan analisis Total Productive Maintenance (TPM) dengan menggunakan metode Overall Equipment Effectiveness (OEE) dan dilakukan analisis diagram sebab akibat (fishbone) dan diagram pareto untuk usulan perbaikan. Hasil analisis OEE Nilai Availability 90,52%, Nilai Performance 63,63%, Nilai Quality 53,09%. Nilai Availability, Nilai Performance, Nilai Quality masih dibawah nilai standar OEE >84%. Nilai OEE yang diperoleh dibawah standar yaitu 30,58%. Faktor yang sangat berpengaruh terhadap rendahnya nilai OEE adalah Nilai Quality yang rendah yaitu 53,09%. Idle and Minor Stoppage Losses yaitu sebesar -50,84%, kemudian Defect Losses sebesar 19,71%, Equipment Failure Losses yaitu sebesar 6,60%, Setup and Adjustment Losses sebesar 2,88%, dan yang terakhir adalah pengurangan kecepatan Reduced Speed Losses yaitu sebesar 57,71%.
{"title":"Overall Equipment Effectiveness Di Desalination Plant 2 Pada PT Sari Dumai","authors":"None Trisna Mesra, Fitra Fitra, Elisa Hafrida, Faisal Ferdiansyah","doi":"10.31289/jime.v7i1.9405","DOIUrl":"https://doi.org/10.31289/jime.v7i1.9405","url":null,"abstract":"Mesin merupakan salah satu peranan penting dalam keberhasilan perusahaan dalam memenuhi permintaan konsumen. Desalination Plant 2 di PT Sari Dumai Sejati merupakan plant pengolahan air laut. Permasalahan yang terjadi adalah rendahnya efesiensi produksi air laut tersebut. Mesin produksi yang digunakan sering mengalami breakdown, breakdown diperoleh dari catridge filter, pompa DMF, pompa SWRO, dan pompa BWRO. Berdasarkan permasalahan tersebut maka dilakukan analisis Total Productive Maintenance (TPM) dengan menggunakan metode Overall Equipment Effectiveness (OEE) dan dilakukan analisis diagram sebab akibat (fishbone) dan diagram pareto untuk usulan perbaikan. Hasil analisis OEE Nilai Availability 90,52%, Nilai Performance 63,63%, Nilai Quality 53,09%. Nilai Availability, Nilai Performance, Nilai Quality masih dibawah nilai standar OEE >84%. Nilai OEE yang diperoleh dibawah standar yaitu 30,58%. Faktor yang sangat berpengaruh terhadap rendahnya nilai OEE adalah Nilai Quality yang rendah yaitu 53,09%. Idle and Minor Stoppage Losses yaitu sebesar -50,84%, kemudian Defect Losses sebesar 19,71%, Equipment Failure Losses yaitu sebesar 6,60%, Setup and Adjustment Losses sebesar 2,88%, dan yang terakhir adalah pengurangan kecepatan Reduced Speed Losses yaitu sebesar 57,71%.","PeriodicalId":479709,"journal":{"name":"JIME (Journal of Industrial and Manufacture Engineering)","volume":"193 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135429247","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}
Perikanan budidaya merupakan salah satu sektor perikanan yang memiliki prospek yang sangat menjanjikan khususnya di Indonesia. Salah satu komoditi perikanan budidaya yang menjadi primadona adalah ikan lele (Clarias sp). Penelitian ini bertujuan untuk mengidentifikasi kejadian risiko pada pembibitan ikan lele dan penyebab risiko guna menghitung nilai risiko sepanjang aliran hulu ke hilir rantai pasok, dan merekomendasikan upaya mitigasi risiko. Lokasi penelitian ini dilakukan di Pembibitan Ikan Lele Kang Arif yang beralamat di L3 Blok B Kecamatan Tenggarong Seberang, Kabupaten Kutai Kartanegara, Kalimantan Timur. Tempat Pembibitan Ikan Lele Kang Arif bergerak dibidang pembudidayaan bibit ikan lele. Bibit ikan lele ini sudah didistribusikan ke wilayah Kutai Kartanegara, bahkan sampai Balikpapan. Proses source terdapat 3 risk agent prioritas dan didapatkan 6 aksi mitigasi. Proses plan terdapat 3 risk agent dominan dan didapatkan 6 aksi mitigasi. Proses make terdapat 3 risk agent dominan dan didapatkan 6 aksi mitigas. Proses deliver terdapat 3 risk agent dominan dan didapatkan 6 aksi mitigasi. Proses Return Terdapat 3 risk agent prioritas dan didapatkan 6 strategi. Pihak pembibitan dapat mengetahui sumber-sumber risiko apa saja yang terdapat di pembibitan. Strategi penanganan/mitigasi yang diusulkan dapat menjadi pertimbangan dalam mengatasi sumber risiko prioritas yang ada di pembibitan. Analisis penilaian performansi (performance appraisal) berdasarkan KPI (Key Performance Indicator) untuk mendukung pelaksanaan mitigasi perusahaan yaitu berupa evaluasi pegawai untuk masing-masing proses supply chain yang ada pada pembibitan Ikan Lele Kang Arif.
{"title":"Manajemen Risiko Rantai Pasok Pembibitan Ikan Lele (Studi Kasus: Pembibitan Ikan Kang Arif)","authors":"Dhimas Saifullah, Wahyuda Wahyuda, Yudi Sukmono","doi":"10.31289/jime.v7i1.8518","DOIUrl":"https://doi.org/10.31289/jime.v7i1.8518","url":null,"abstract":"Perikanan budidaya merupakan salah satu sektor perikanan yang memiliki prospek yang sangat menjanjikan khususnya di Indonesia. Salah satu komoditi perikanan budidaya yang menjadi primadona adalah ikan lele (Clarias sp). Penelitian ini bertujuan untuk mengidentifikasi kejadian risiko pada pembibitan ikan lele dan penyebab risiko guna menghitung nilai risiko sepanjang aliran hulu ke hilir rantai pasok, dan merekomendasikan upaya mitigasi risiko. Lokasi penelitian ini dilakukan di Pembibitan Ikan Lele Kang Arif yang beralamat di L3 Blok B Kecamatan Tenggarong Seberang, Kabupaten Kutai Kartanegara, Kalimantan Timur. Tempat Pembibitan Ikan Lele Kang Arif bergerak dibidang pembudidayaan bibit ikan lele. Bibit ikan lele ini sudah didistribusikan ke wilayah Kutai Kartanegara, bahkan sampai Balikpapan. Proses source terdapat 3 risk agent prioritas dan didapatkan 6 aksi mitigasi. Proses plan terdapat 3 risk agent dominan dan didapatkan 6 aksi mitigasi. Proses make terdapat 3 risk agent dominan dan didapatkan 6 aksi mitigas. Proses deliver terdapat 3 risk agent dominan dan didapatkan 6 aksi mitigasi. Proses Return Terdapat 3 risk agent prioritas dan didapatkan 6 strategi. Pihak pembibitan dapat mengetahui sumber-sumber risiko apa saja yang terdapat di pembibitan. Strategi penanganan/mitigasi yang diusulkan dapat menjadi pertimbangan dalam mengatasi sumber risiko prioritas yang ada di pembibitan. Analisis penilaian performansi (performance appraisal) berdasarkan KPI (Key Performance Indicator) untuk mendukung pelaksanaan mitigasi perusahaan yaitu berupa evaluasi pegawai untuk masing-masing proses supply chain yang ada pada pembibitan Ikan Lele Kang Arif.","PeriodicalId":479709,"journal":{"name":"JIME (Journal of Industrial and Manufacture Engineering)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135429243","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}
Klinik Pratama Arifa Medikal terus mengalami peningkatan pasien setiap bulannya selama satu tahun terakhir dan hingga saat ini telah mengelola 104 jenis obat. Pada saat ini, perencanaan dalam menyediakan obat pada Klinik Pratama Arifa medikal belum menggunakan analisis kebutuhan obat sehingga sulit untuk memperkirakan jumlah pemesanan obat yang efisien. Hal ini mengakibatkan terjadinya stock out obat-obat tertentu sehingga menurunnya kepuasan pelanggan dalam menerima pelayanan pada Klinik Arifa Medikal dan secara tidak langsung mengurangi pendapatan klinik. Tujuan penelitian ini adalah melakukan pengendalian persediaan obat menggunakan metode EOQ (Economic Order Quantity) untuk obat-obat kritis berdasarkan klasifikasi obat menggunakan metode ABC (Always, Better, Control) dan VEN (Vital, Essensial, Non-Essensial). Berdasarkan analisis ABC, obat kelas A berjumlah 30 jenis (69,68%), kelas B sebanyak 49 jenis obat (20,39%) dan selebihnya masuk dalam kelas C dengan jumlah 25 jenis obat (9,93%). Obat yang masuk kategori atau kelas A selanjutnya dianalisis mengunakan VEN. Analisis VEN menunjukkan bahwa terdapat 17 dari 30 jenis obat yang tergolong kategori vital (V). Obat yang masuk kelas A dan kategori vital selanjutnya dilakukan perencanaan persediaan dengan metode EOQ. Hasil perhitungan dengan metode EOQ memberikan hasil, terjadi penghematan biaya sebesar Rp. 308.247/bulan atau setara 19,18%.
{"title":"Perencanaan Pengendalian Persediaan Obat pada Klinik Pratama Arifa Medikal","authors":"Khairul Anshar, Syarifah Akmal, Sucifira Alpinna","doi":"10.31289/jime.v7i1.9492","DOIUrl":"https://doi.org/10.31289/jime.v7i1.9492","url":null,"abstract":"Klinik Pratama Arifa Medikal terus mengalami peningkatan pasien setiap bulannya selama satu tahun terakhir dan hingga saat ini telah mengelola 104 jenis obat. Pada saat ini, perencanaan dalam menyediakan obat pada Klinik Pratama Arifa medikal belum menggunakan analisis kebutuhan obat sehingga sulit untuk memperkirakan jumlah pemesanan obat yang efisien. Hal ini mengakibatkan terjadinya stock out obat-obat tertentu sehingga menurunnya kepuasan pelanggan dalam menerima pelayanan pada Klinik Arifa Medikal dan secara tidak langsung mengurangi pendapatan klinik. Tujuan penelitian ini adalah melakukan pengendalian persediaan obat menggunakan metode EOQ (Economic Order Quantity) untuk obat-obat kritis berdasarkan klasifikasi obat menggunakan metode ABC (Always, Better, Control) dan VEN (Vital, Essensial, Non-Essensial). Berdasarkan analisis ABC, obat kelas A berjumlah 30 jenis (69,68%), kelas B sebanyak 49 jenis obat (20,39%) dan selebihnya masuk dalam kelas C dengan jumlah 25 jenis obat (9,93%). Obat yang masuk kategori atau kelas A selanjutnya dianalisis mengunakan VEN. Analisis VEN menunjukkan bahwa terdapat 17 dari 30 jenis obat yang tergolong kategori vital (V). Obat yang masuk kelas A dan kategori vital selanjutnya dilakukan perencanaan persediaan dengan metode EOQ. Hasil perhitungan dengan metode EOQ memberikan hasil, terjadi penghematan biaya sebesar Rp. 308.247/bulan atau setara 19,18%.","PeriodicalId":479709,"journal":{"name":"JIME (Journal of Industrial and Manufacture Engineering)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135429256","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}
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