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MODEL HOT (HUMAN, ORGANIZATION, TECHNOLOGY) FIT UNTUK EVALUASI PENERAPAN APLIKASI SIPANDAI UNTUK PENGGUNA DOSEN (STUDI KASUS : UNIVERSITAS CATUR INSAN CENDEKIA KOTA CIREBON) Devi, Silviana; Marsani Asfi; Mesi Febima
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 10 No. 2 (2024): Volume 10 Nomor 2
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v10i2.3268

Universitas Catur Insan Cendekia telah menerapkan e-learning melalui Sistem Informasi Pembelajaran dan Administrasi Terintegrasi (SIPANDAI). Namun, terdapat beberapa permasalahan dalam aplikasi SIPANDAI yaitu adanya perbedaan penguasaan komputer dan kurangnya feedback dalam sosialisasi, kurang sistematis saat terjadi kesalahan data dan perlu adanya pengembangan beberapa fitur seperti pada fitur kelola pertemuan, fitur generate absen dan fitur cancel. Adapun tujuan penelitian ini adalah mengevaluasi aplikasi SIPANDAI untuk menilai sejauh mana kelayakan dan kesuksesan penerapan aplikasi SIPANDAI di Universitas Catur Insan Cendekia. Metode yang digunakan dalam penelitian ini adalah Model HOT (Human, Organization, Technology) FIT. Model ini melibatkan 3 aspek utama yaitu Human, Organization, Technology yang memiliki beberapa variabel seperti Pengguna Sistem, Kepuasan Pengguna, Stuktur Organisasi, Lingkungan Oragnisasi, Kualitas Sistem, Kualitas Informasi, Kualitas Layanan dan Manfat Bersih. Hasil penelitian ini, variabel yang berpengaruh kuat pada keberhasilan penerapan aplikasi SIPANDAI adalah variabel Kualitas Informasi terhadap Pengguna Sistem dengan nilai 0,743. Dan variabel yang berpengaruh lemah adalah variabel Struktur Organisasi terhadap Manfaat Bersih dengan nilai 0,421. Oleh karena itu, diperoleh rekomendasi yang dapat diberikan ke bagian pusdatin untuk melakukan perbaikan dan pengembangan pada fitur aplikasi SIPANDAI. Kata Kunci : E-Learning, UCIC, Model HOT Fit, Evaluasi, SIPANDAI

SISTEM PENUNJANG KEPUTUSAN DALAM PENENTUAN RUTE DAN KAPASITAS MUATAN DISTRIBUSI DENGAN MENGGUNAKAN METODE SAVING MATRIX DAN NEAREST NEIGHBOAR PADA CV BINTANG BERKAH CIREBON ., Nico Firmansyah; Lena Magdalena; Mesi Febima
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 10 No. 2 (2024): Volume 10 Nomor 2
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v10i2.3271

This research creates and designs a Decision Support System (SPK) for CV Bintang Berkah by combining the Saving Matrix and Nearest Neighbor methods to optimize the route and capacity of the distribution load. CV Bintang Berkah faces challenges in managing distribution costs and load capacity, which affects operational efficiency. The Saving Matrix method helps in determining distribution routes with distance and cost savings, while the Nearest Neighbor method optimizes the order of visits based on the closest distance. The implementation of this system is expected to reduce transportation costs, increase distribution efficiency, and improve service quality. This system uses PHP and MySQL, focusing on the Cirebon City and Regency areas. The results show that applying this method significantly reduces the distance and cost of distribution and increases efficiency in the distribution process. With this system, CV Bintang Berkah is expected to overcome distribution challenges better, save costs, and increase the company's profitability.

SISTEM INFORMASI FORECASTING DATA PENJUALAN KENDARAAN MENGGUNAKAN METODE SINGLE EXPONENTIAL SMOOTHING (STUDI KASUS: PT. SENDANG SUMBER ARUM VIAR MOTOR CIREBON) Lestari, Lina; Lena Magdalena; Mesi Febima
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 10 No. 2 (2024): Volume 10 Nomor 2
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v10i2.3283

Limited Liability Companies (PT) are found in almost all regions in Indonesia, one of which is PT. Sendang Sumber Arum 'VIAR Motor which provides sales of Karya 3-wheeled motorcycle units, e-motorcycles, razors, Cross Adventure, vintech, and e-bikes. E-bikes are the best-selling vehicles for each period, especially the UNO3 type. The problem faced by this company is the imbalance in sales figures which causes damage to the e-bike batteries that are sold over a long period of time and requires forecasting. The Single Exponential Smoothing method is the right forecasting method used to predict demand for goods that change very quickly, which aims to determine the estimated availability of vehicle units that must be held in the future, based on previous sales data. In determining the error value in forecasting, the author uses the Mean Square Error (MSE) which is based on the alpha value. This forecasting is implemented into an information system that produces a forecast for the UNO3 type e-bike with the smallest Mean Square Error (MSE) value obtained with an alpha of 0.3, namely with a value of 167.294. This proves the best forecast for predicting the quantity of UNO3-type e-bike stock units at PT. Sendang Sumber Arum ‘VIAR Motor’ Cirebon for the period of June 2024 using alpha 0.3. So the forecast value of UNO3 type e-bike unit sales for June 2024 in the 11-month forecast period with alpha 0.3 is 24.89 or around 25 units with actual data.

Implementasi Optimasi NLP dan KNN untuk User Review Aplikasi SAMPEAN Cirebon Mesi Febima; Lena Magdalena; Marsani Asfi; Muhammad Hatta; Rifqi Fahrudin
Prosiding SISFOTEK Vol 8 No 1 (2024): SISFOTEK VIII 2024
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

The use of information technology in the personnel administration process plays an active role in improving public services for civil servants (ASN) in the Cirebon City Government by providing accurate data for decision-making. One of the smart city applications that assists ASN in Cirebon City in supporting personnel administration activities is the SAMPEAN Cirebon City application. However, to ensure that this application is truly effective and meets user needs, it is important to analyze user reviews provided through application reviews. One effective method for analyzing user reviews is by using Natural Language Processing (NLP) and machine learning techniques. The NLP technique and classification model used is the KNN algorithm. The purpose of this research is to provide valuable input for application developers in improving the quality and performance of the SAMPEAN application. The research results show that by testing accuracy using the confusion matrix with K values of 3, 5, 7, and 9, it was found that K=9 provides the best performance with a balance between precision, recall, F1-Score, and accuracy. The model achieved a precision of 64%, recall of 90%, F1-Score of 75%, and accuracy of 62%. It can be concluded that with the optimization of the K parameter in KNN, the higher the K value, the higher the accuracy. This emphasizes the importance of selecting the right parameters to enhance the effectiveness of machine learning models in various Natural Language Processing (NLP) applications.

Implementation of the Haversine Formula Method in Geographic Information Systems for Searching the Nearest Sea Freight Expedition Services in East Jakarta Efriza Yunardi; Lena Magdalena; Mesi Febima
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 1 (2024): October 2024
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i1.622

In export and import activities, a partner is needed for the shipping process. There are three types of shipping routes available for export and import: land, sea, and air. A company that offers shipping services via sea routes is known as an EMKL. This study designs a Geographic Information System (GIS) to facilitate the search for locations of Marine Cargo Expedition Services (EMKL) in East Jakarta using the Haversine Formula. The main problem faced is the difficulty in finding nearby and relevant expedition service providers in a large and densely populated area like East Jakarta. The proposed solution involves developing a system that integrates location data with the Haversine Formula to accurately calculate the distance between the user's location and the service providers. By using this method, the system can provide precise location information and help users select the nearest expedition service. The goal of this research is to enhance efficiency and accuracy in locating expedition services. The expected outcome is the creation of a user-friendly and effective system that simplifies the process of selecting expedition services by considering distance and service availability in real-time.

MODEL HOT (HUMAN, ORGANIZATION, TECHNOLOGY) FIT UNTUK EVALUASI PENERAPAN APLIKASI SIPANDAI UNTUK PENGGUNA DOSEN (STUDI KASUS : UNIVERSITAS CATUR INSAN CENDEKIA KOTA CIREBON) Devi, Silviana; Marsani Asfi; Mesi Febima
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 10 No. 2 (2024): Volume 10 Nomor 2
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v10i2.3268

Universitas Catur Insan Cendekia telah menerapkan e-learning melalui Sistem Informasi Pembelajaran dan Administrasi Terintegrasi (SIPANDAI). Namun, terdapat beberapa permasalahan dalam aplikasi SIPANDAI yaitu adanya perbedaan penguasaan komputer dan kurangnya feedback dalam sosialisasi, kurang sistematis saat terjadi kesalahan data dan perlu adanya pengembangan beberapa fitur seperti pada fitur kelola pertemuan, fitur generate absen dan fitur cancel. Adapun tujuan penelitian ini adalah mengevaluasi aplikasi SIPANDAI untuk menilai sejauh mana kelayakan dan kesuksesan penerapan aplikasi SIPANDAI di Universitas Catur Insan Cendekia. Metode yang digunakan dalam penelitian ini adalah Model HOT (Human, Organization, Technology) FIT. Model ini melibatkan 3 aspek utama yaitu Human, Organization, Technology yang memiliki beberapa variabel seperti Pengguna Sistem, Kepuasan Pengguna, Stuktur Organisasi, Lingkungan Oragnisasi, Kualitas Sistem, Kualitas Informasi, Kualitas Layanan dan Manfat Bersih. Hasil penelitian ini, variabel yang berpengaruh kuat pada keberhasilan penerapan aplikasi SIPANDAI adalah variabel Kualitas Informasi terhadap Pengguna Sistem dengan nilai 0,743. Dan variabel yang berpengaruh lemah adalah variabel Struktur Organisasi terhadap Manfaat Bersih dengan nilai 0,421. Oleh karena itu, diperoleh rekomendasi yang dapat diberikan ke bagian pusdatin untuk melakukan perbaikan dan pengembangan pada fitur aplikasi SIPANDAI. Kata Kunci : E-Learning, UCIC, Model HOT Fit, Evaluasi, SIPANDAI

SISTEM PENUNJANG KEPUTUSAN DALAM PENENTUAN RUTE DAN KAPASITAS MUATAN DISTRIBUSI DENGAN MENGGUNAKAN METODE SAVING MATRIX DAN NEAREST NEIGHBOAR PADA CV BINTANG BERKAH CIREBON ., Nico Firmansyah; Lena Magdalena; Mesi Febima
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 10 No. 2 (2024): Volume 10 Nomor 2
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v10i2.3271

This research creates and designs a Decision Support System (SPK) for CV Bintang Berkah by combining the Saving Matrix and Nearest Neighbor methods to optimize the route and capacity of the distribution load. CV Bintang Berkah faces challenges in managing distribution costs and load capacity, which affects operational efficiency. The Saving Matrix method helps in determining distribution routes with distance and cost savings, while the Nearest Neighbor method optimizes the order of visits based on the closest distance. The implementation of this system is expected to reduce transportation costs, increase distribution efficiency, and improve service quality. This system uses PHP and MySQL, focusing on the Cirebon City and Regency areas. The results show that applying this method significantly reduces the distance and cost of distribution and increases efficiency in the distribution process. With this system, CV Bintang Berkah is expected to overcome distribution challenges better, save costs, and increase the company's profitability.

SISTEM INFORMASI FORECASTING DATA PENJUALAN KENDARAAN MENGGUNAKAN METODE SINGLE EXPONENTIAL SMOOTHING (STUDI KASUS: PT. SENDANG SUMBER ARUM VIAR MOTOR CIREBON) Lestari, Lina; Lena Magdalena; Mesi Febima
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 10 No. 2 (2024): Volume 10 Nomor 2
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v10i2.3283

Limited Liability Companies (PT) are found in almost all regions in Indonesia, one of which is PT. Sendang Sumber Arum 'VIAR Motor which provides sales of Karya 3-wheeled motorcycle units, e-motorcycles, razors, Cross Adventure, vintech, and e-bikes. E-bikes are the best-selling vehicles for each period, especially the UNO3 type. The problem faced by this company is the imbalance in sales figures which causes damage to the e-bike batteries that are sold over a long period of time and requires forecasting. The Single Exponential Smoothing method is the right forecasting method used to predict demand for goods that change very quickly, which aims to determine the estimated availability of vehicle units that must be held in the future, based on previous sales data. In determining the error value in forecasting, the author uses the Mean Square Error (MSE) which is based on the alpha value. This forecasting is implemented into an information system that produces a forecast for the UNO3 type e-bike with the smallest Mean Square Error (MSE) value obtained with an alpha of 0.3, namely with a value of 167.294. This proves the best forecast for predicting the quantity of UNO3-type e-bike stock units at PT. Sendang Sumber Arum ‘VIAR Motor’ Cirebon for the period of June 2024 using alpha 0.3. So the forecast value of UNO3 type e-bike unit sales for June 2024 in the 11-month forecast period with alpha 0.3 is 24.89 or around 25 units with actual data.

MODEL PENGUKURAN KINERJA RANTAI PASOK BERBASIS GREEN SCOR DAN FUZZY AHP: STUDI KASUS PT. ARTERIA DAYA MULIA Suci Nurpatimah; Lena Magdalena; Mesi Febima
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 11 No. 2 (2025): Volume 11 Nomor 2 Tahun 2025
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Supply chain performance measurement plays a crucial role in supporting operational continuity and corporate competitiveness, especially in meeting the demands for efficiency, effectiveness, and environmental sustainability. Imbalances in supply chain management can lead to resource waste, environmental pollution, and decreased customer satisfaction. PT. Arteria Daya Mulia, a rope manufacturing company, currently lacks a supply chain performance measurement system that fully incorporates sustainability aspects. This study aims to design a performance measurement model based on the Green SCOR framework and the Fuzzy AHP method as a strategic decision-making tool that considers sustainability dimensions. Performance indicators were determined according to the five main Green SCOR processes (Plan, Source, Make, Deliver, and Return), comprising 14 KPIs developed through literature review and field validation. Data were collected through observations, interviews, and questionnaires, then processed using the Fuzzy AHP method to obtain the priority weight of each indicator. The results show that the total supply chain performance score is 88, calculated by combining the weights with the Snorm de Boer values. Several indicators demonstrated excellent performance with a maximum Snorm value (100). However, one critical indicator was identified with the lowest Snorm value—% Error-free Return Shipped in the Return process—scoring 0.02 with a final SCM score of 0.0008, indicating the need for immediate improvement. The developed information system also generates automatic improvement recommendations based on the measurement results. This model is expected to assist the company in monitoring, evaluating, and continuously improving supply chain performance.

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