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All Journal JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Jusikom: Jurnal Sistem Informasi Ilmu Komputer
Nababan, Marlince
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Computer Science & IT Decision Sciences, Operations Research & Management

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Herbal Leaf Image Classification Using Convolutional Neural Network (CNN) Mujahid, Putra Edi; Manik, Rosianni; Simbolon, Junpri Sardodo; Sinaga, Maria Riska Ratna Sari; Aisyah, Siti; Nababan, Marlince; Harmaja, Okta Jaya
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 8 No. 1 (2024): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v8i1.5145

Abstract

This research delves into the application of Convolutional Neural Networks (CNNs) to address the complexities of identifying herbal leaf species in Indonesia, often challenging due to the vast variations in shape, color, and texture. Utilizing a dataset of herbal leaf images acquired using the Bing Downloader Scrapping technique, a CNN model was trained to classify various plant varieties with a remarkable accuracy rate of 92.66%. Additionally, the analysis of low loss values indicates that the model not only effectively maps the intricate features of each image to the correct category but also efficiently reduces error rates. These findings offer a significant contribution to the context of herbal medicine development and biodiversity conservation, opening up avenues for technological integration in efforts to preserve Indonesia's natural and cultural resources.
Aplikasi Penentuan Karyawan Terbaik dengan Metode AHP dan Metode Promethee Nababan, Marlince
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 1 No. 2 (2018): JURNAL SISTEM INFROMASI DAN ILMU KOMPUTER PRIMA (JUSIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (705.059 KB)

Abstract

Sistem Pendukung Keputusan di defenisikan sebuah sistem yang mampu menghasilkan pemecahan masalah maupun penanganan masalah, salah satu dari pendukung keputusan yaitu metode Analytical Hierarchy Process (AHP) dan Promethee. Proses penilaian penerimaan karyawan menghabiskan banyak waktu dan penilaian dilakukan masih secara subjektif. Dengan penerapan metode AHP dan Promethee dalam hal pengangkatan karyawan tetap dan yang layak, ada beberapa variabel atau kriteria penetapan pengangkatan karyawan tetap yaitu umur, pendidikan, kehadiran, pengalaman kerja dan loyalitas sebagai tolak ukur pengangkatan karywan teta dengan nilai maksimum 0.411 dan nilai minimum -0.341 sehingga metode AHP dan Promethee dapat membantu pengambilan keputusan.
COMPARISON OF SINGLE EXPONENTIAL SMOOTHING METHOD WITH DOUBLE EXPONENTIAL SMOOTHING METHOD PREDICTION OF SALT SALES Harly, Jesslyn; Nababan, Marlince; Bintang, Lidya Haryati; Rizal, Reyhan Achmad; -, Aisyah
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 6 No. 2 (2023): JURNAL SISTEM INFROMASI DAN ILMU KOMPUTER PRIMA (JUSIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v6i2.3366

Abstract

Predicting the quantity of product sales in the future aims to control the amount of existing product stock, so that the shortage or excess of product stock can be minimized. When the quantity of sales can be predicted accurately, the fulfillment of consumer demand can be managed in a timely manner and the company's cooperation with consumers is maintained properly so that the company can avoid losing sales and consumers. This study aims to analyze the accuracy of predicting the quantity of sales of salt using the Single Exponential Smoothing (SES) method compared to using the Double Exponential Smoothing (DES) method, so that a more accurate method will be obtained for predicting the quantity of sales. The results of testing the comparison of the level of accuracy can be done by evaluating the error value of the forecasting results with the Mean Absolute Percentage Error (MAPE). The lowest MAPE result obtained is in the SES method when the parameter α = 0.054 with a MAPE result of 7.932% which means the accuracy value is very accurate. Whereas with the DES method the MAPE value is 28.145% while the parameter α = 0.845 β = 0.214 which means the value of accuracy is reasonable. Based on the MAPE results obtained using the two methods above, the Single Exponential Smoothing method is more accurate for use in predicting salt sales. Whereas with the DES method the MAPE value is 28.145% while the parameter α = 0.845 β = 0.214 which means the value of accuracy is reasonable. Based on the MAPE results obtained using the two methods above, the Single Exponential Smoothing method is more accurate for use in predicting salt sales. Whereas with the DES method the MAPE value is 28.145% while the parameter α = 0.845 β = 0.214 which means the value of accuracy is reasonable. Based on the MAPE results obtained using the two methods above, the Single Exponential Smoothing method is more accurate for use in predicting salt sales
APPLICATION OF THE K-MEANS CLUSTERING METHOD FOR PERFORMANCE ASSESSMENT BASED ON EDUCATOR COMPETENCE Erikson, Paul; Angkat, Bobby Rahman; Yosua, Eliza Christovel; Sembiring, Mutiara; Nababan, Marlince
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 7 No. 1 (2023): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v7i1.3869

Abstract

Performance appraisal is one thing to respect someone while working in an institution, one of which is a private higher education institution. To respect the performance of resources, there needs to be a value assigned to someone. Assessments carried out for one semester need to be reviewed again because during filling in the student assessments do not fill in according to their understanding so that a review needs to be carried out again. The assessment was carried out using the K-Means method by applying the concept of the centroid value. There are 4 (four) variables used, namely pedagogic competence, personal competence, social and professional competence with a value of K = 3. The maximum number of observations for cluster 3 is 368 while the value of Distances Between Cluster Centroids shows 2 suitable clusters, namely cluster 1 and cluster 2, which is 1.7020. The author gives suggestions to remove outlier data before entering the data to be trained into the algorithm to improve visualization if the dataset is large. Key Word: Performance Appraisal, Data Mining, K-Means
APPLICATION OF THE K-MEANS CLUSTERING METHOD FOR PERFORMANCE ASSESSMENT BASED ON THE COMPETENCY OF EDUCATORS Nababan, Marlince; Sinaga, Cristian Vieri; Napitupulu, Indra Wirayudha; Rahman, Adzisyah; -, Steven
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 7 No. 1 (2023): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v7i1.3951

Abstract

The ability of lecturers to teach for 1 (one) semester needs to be evaluated. Evaluation is a student assessment through a student academic information system. Review using the system is separate because many students need to care about the filled things. There the author tries to analyze the values ​​from the questionnaire results distributed to students using the Principal Component Analysis (PCA) technique and the K-Means method, where PCA reduces data. At the same time, K-Means assumed the values ​​closest to the study's results and concluded that of the 3 (three) clusters, the maximum distance was 0.5475 in cluster 1 and cluster 3.
LIVER DISEASE CLASSIFICATION ANALYSIS USING THE XGBOOST METHOD Sitinjak, Yadi; -, Muhaymin; Nababan, Marlince
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 7 No. 1 (2023): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v7i1.4130

Abstract

Liver disease is a severe pathological condition that can cause liver inflammation due to viral infection, toxic agents, or bacterial invasion, interfering with normal liver function. The death rate from this disease reaches 1.2 million people annually in Southeast Asia and Africa. Liver disease can cause damage to the liver and negatively affect overall body function. To reduce disease progression, it is critical to facilitate early diagnosis, thereby enabling rapid initiation of treatment for affected individuals. Classification methods are widely used to make decisions based on new information from previous data processing through calculation algorithms. This study uses the XGBoost classification method to build a predictive model for liver disease. The results of this study confirm that the XGBoost model is a robust and efficient choice for liver disease classification based on patient data. The use of the XGBoost approach has proven its success in the category of liver disease with an accuracy of up to 95% and an accuracy balance of 95%, demonstrating the effectiveness and efficiency of this method in overcoming class imbalances in liver disease classification data.   Keywords: Xgboost, Liver, Classification, Disease
Adaptive Web-Based Information System for MBKM Program Using Scrum Putri, Riska; -, Okta; khairunisa, farinnadiya; -, Cristopher; Nababan, Marlince
JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Vol. 8 No. 1 (2025): Jurnal Teknologi dan Ilmu Komputer Prima (JUTIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jutikomp.v8i1.7131

Abstract

The implementation of the “Merdeka Belajar Kampus Merdeka” (MBKM) program requires an integrated and adaptive information system to manage academic activities efficiently. This study aims to develop a web-based MBKM Management Information System at Universitas Prima Indonesia using the Scrum framework. The system supports structured registration, reporting, assessment, and verification processes involving students, academic advisors, department heads, and MBKM administrators. Technologies used include React.js and Next.js for the frontend, Express.js for the backend, and MySQL for the database. A key contribution of this research is the application of the agile Scrum methodology to accommodate the dynamic nature of MBKM policies, ensuring iterative development and user-centered design. Internal testing using Black Box methods indicates high functionality and usability. The resulting system enhances efficiency, data integration, and stakeholder collaboration. This study demonstrates how agile practices can be effectively applied to develop scalable academic systems that support national education reforms.
Co-Authors -, Cristopher -, Muhaymin -, Okta -, Steven Aisyah - Angkat, Bobby Rahman Bintang, Lidya Haryati Erikson, Paul Harly, Jesslyn khairunisa, farinnadiya Manik, Rosianni Mujahid, Putra Edi Napitupulu, Indra Wirayudha Okta Jaya Harmaja Rahman, Adzisyah Riska Putri Rizal, Reyhan Achmad Sembiring, Mutiara Simbolon, Junpri Sardodo Sinaga, Cristian Vieri Sinaga, Maria Riska Ratna Sari Siti Aisyah Sitinjak, Yadi Yosua, Eliza Christovel