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All Journal InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Sisfokom (Sistem Informasi dan Komputer) Jusikom: Jurnal Sistem Informasi Ilmu Komputer Jurnal Sains dan Teknologi REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer INFOKUM Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer
Banjarnahor, Jepri
Universitas Prima Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Mathematics Physics Social Sciences

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Implementasi Metode Selection Sort Dalam Sistem Repository Skripsi Banjarnahor, Jepri; Bawamenewi, Deskarya; Tanoto, Carvin; NK Nababan, Marlince; Purba, Windania; aisyah, siti
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 5 No. 2 (2022): 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.v5i2.2389

Abstract

Abstrak Sistem informasi repository Universitas Prima Indonesia pada saat ini masih menggunakan sistem manual, yakni pada proses pendistribusian nya yang masih harus datang ke perpustakaan dalam mengakses skripsi hasil penelitian alumni. Oleh sebab itu dibutuhkan manajemen repositori yang baik. Tujuan perancangan system informasi repository ini untuk mempermudah mahasiswa untuk mengakses secara online skripsi dari hasil penelitian alumni kampus Universitas Prima Indonesia. Pengembangan sistem informasi ini menggunakan metode Selection Sort. Pada tahap analisa dan perancangannya menggunakan pemodelan Unified Modeling Language. Pengumpulan data dilakukan dengan wawancara langsung dengan mahasiswa dan bagian Perpustakaan, dari hasil pengumpulan data tersebut didapatkan informasi prosedur yang sedang berjalan dan kendala yang dihadapi oleh mahasiswa dan bagian Perpustakaan Universitas Prima Indonesia, kemudian data tersebut dianalisa dan digunakan sebagai informasi untuk merancang sistem. Proses perancangan sistem dilakukan dengan menggunakan bahasa pemrograman PHP dan database MySQL dan metode Bubble Sort dengan hasil penelitian ini adalah berupa rancangan sistem informasi repository skripsi berbasis web yang dapat memudahkan mahasiswa di dalam mengakses skripsi alumni Universitas Prima Indonesia.
Analysis of Method C5.0 in Triggering Factors The Number of Covid-19 Increases or Decreases After Getting the Vaccine Tanzil, Alferedo; Barasa, Randy Aldany; Laia, Yonata; Banjarnahor, Jepri
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.v6i2.3410

Abstract

Because we now know that many things make everyone sick, such as fever, flu, cough, and other diseases that are said to be easily transmitted, we need a system that can overcome the above problems. This study uses the K-NN method to examine what factors influence the increase in the number of people infected with Covid-19. The factors tested in this study were frequent violations of health practices, overcrowding, and weak immune systems. The K-NN method can overcome the problem of knowing the factors causing the increase in Covid-19 patients after vaccination.
K-NEAREST NEIGHBOR (KNN) ANALYSIS FOR CLOTHING SALES CLASSIFICATION BASED ON MATERIALS USED Banjarnahor, Jepri; Siregar, Regina; Lumbantobing, Christian Frederic; Ridho, Muhammad Alfathan; Zuhdi, Muhammad Fikri Akbar
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.v7i1.3903

Abstract

Abstract Classification is a method to see the behavior and characteristics of certain groups. The K-nearest neighbor method is a learning algorithm for classifying new data based on the K-Nearest Neighbor majority class. The main purpose of this algorithm is to classify new objects based on attributes and training samples. In today's digital era, competition in the business world is getting tougher and growing rapidly, especially when it comes to online marketing systems. Every market driver must always pay attention to the needs and desires of consumer satisfaction when buying products from online stores. However, the problem that consumers often complain about is the use of clothing size charts in online stores that do not match the consumer's body size. This study aims to reduce the frustration of consumers buying clothes online and in such a way that products do not have to be returned via the internet. Based on these problems, these conditions must be improved by selecting clothes to achieve optimal customer satisfaction. This application was built using the K-Nearest Neighbor (KNN) method and Profile Matching to help you determine what clothes are most suitable for your consumer size.
IMPLEMENTATION OF DATA MINING TO PREDICT THE VALUE OF INDONESIAN OIL AND NON-OIL AND GAS IMPORT EXPORTS USING THE LINEAR REGRESSION METHOD Ompusunggu, Elvis Sastra; Sinaga, Wilson; Siahaan, Mikael; Banjarnahor, Jepri; Winata, Jaspin; Laia, Yonata; Sihombing, Oloan
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.v7i1.4081

Abstract

Indonesia's export-import activities in recent years, the value of Indonesia's exports and imports has decreased due to global conditions. The problems that occur are the uncertainty and complexity in estimating the value of international trade in the oil and gas and non-oil and gas sectors, dependence on just one or a few markets, and the problem of unfair competition, unfair competition between business actors can reduce export-import prices. The value of oil and gas and non-oil and gas exports and imports is influenced by several external factors that are difficult to predict, such as fluctuations in oil and gas prices, changes in trade policies, and global economic factors. The prediction results are obtained every month from the export value data using the rapid miner application. From the export data, the value of non-oil and gas exports obtains a very high value compared to the export data of oil and gas values. Then the results from rapid miner using the linear regression algorithm are obtained. The predicted import value of oil and gas and non-oil and gas value data in June is 209,162,268, and the predicted export value of oil and gas and non-oil and gas value data in June is 349,285,781 and non-oil and gas which more are predicted to have the highest value compared to the value of oil and gas in each month.
ANALYSIS OF CLASSIFICATION OF LUNG CANCER USING THE DECISION TREE CLASSIFIER METHOD Setiawan, Wendy; Banjarnahor, Jepri; Shandika , Muhammad Faja; -, Amalia; Radhi, Muhammad
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.v7i1.4136

Abstract

The International Agency for Research on Cancer (IARC) revealed staggering figures, with 19.3 million global cancer cases and 10 million related deaths in that year. Cancer, characterized by abnormal cell growth, can potentially be dangerous with the ability to metastasize. Notably, lung cancer is often detected in an advanced stage due to a lack of awareness and comprehensive medical assessment. Lung cancer usually presents with a late-stage diagnosis. From 60% to 85% of individuals diagnosed with lung cancer show a lack of awareness about their condition. Early diagnosis using an accurate classification method can significantly increase the success of lung cancer diagnosis. To improve predictions, Decision Tree Classifier method was used in lung cancer classification, resulting in a significant increase in accuracy. This study achieved a good level of accuracy, with an accuracy value of 95.16% at a max_depth model depth of 15, and tested in 40 experimental iterations. These results are expected to provide hope for progress in the classification of lung cancer.   Keywords: Lung, Cancer, Classification, Decision Tree
MODERN APPLICATION FOR IMPROVING AND REHABILITATING PRISONERS' MENTAL HEALTH Yonata Laia; Jepri Banjarnahor; Oloan Sihombing; Haposan Lumbantoruan
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 4 (2025): JITK Issue May 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i4.6595

Abstract

This study evaluates the Fuzzy Tsukamoto method as an effective rehabilitation solution for young inmates facing mental health challenges, including pre-existing conditions, confinement stress, and educational deficits. Mental health issues in correctional facilities remains a growing concern, affecting not only the well-being of inmates but also their chances of successful reintegration into society. The method employs Electroencephalogram/EEG to monitor tracked brain activity, providing real-time data that refined the treatment protocols and allowed for personalized adjustments. Conducted in a correctional facility in Medan, Indonesia, the study found significant reductions in anxiety and depression among participants, along with improved self-efficacy and emotional resilience. The results highlight the potential of the Fuzzy Tsukamoto method in not only improving inmate mental health but also in reducing recidivism rates and supporting social reintegration. These findings underscore the critical need to adopt more rehabilitative correctional strategies to address the complex mental health challenges within the incarcerated population.
Analyzing Consumer Shopping Interest via Social Media Ads with K-Means and C4.5 Algorithm Banjarnahor, Jepri; Hutagalung, Jessy Putrionom; Sitorus, Ferdinand Jery Wilkinson
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 3 (2024): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i3.2228

Abstract

It is increasingly important to understand how advertisements affect consumers' propensity to shop as social media becomes the primary medium for advertising. This study uses the C4.5 algorithm for classification and K-Means Clustering for data segmentation to examine the level of consumer shopping interest driven by Facebook and Instagram ads. This strategy utilizes information collected from user interactions with ads on these two social media platforms to determine consumer interest trends more precisely. The research findings show that, compared to conventional methods, this combination of techniques can increase the accuracy of predicting consumer purchase intention by as much as 85%. These results not only validate the usefulness of clustering and classification methods in digital advertising data analysis, but also offer insights that companies can apply to optimize their marketing strategies. By understanding more specific consumer segments, companies can target their ads more precisely, thereby increasing conversions and the effectiveness of advertising campaigns. This research makes a significant contribution to the field of data analysis and digital marketing and opens up opportunities for further research in the integration of more sophisticated analysis methods
Analisis Kinerja Komparatif Metode Machine Learning Dalam Klasifikasi Sentimen Terhadap Ulasan Aplikasi Dompet Digital Yanto, Willi; Panjaitan, Mega Lastarida; Khosandy, Vincent; Banjarnahor, Jepri
Jurnal Sistem Komputer dan Informatika (JSON) Vol 6, No 4 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v6i4.8831

Abstract

Saat ini, kemajuan teknologi telah merambah di berbagai aspek kehidupan, termasuk sektor keuangan. Salah satu teknologi keuangan yang populer digunakan di Indonesia adalah dompet digital. Penggunaan aplikasi dompet digital memungkinkan transaksi keuangan dilakukan secara daring tanpa perlu menggunakan uang tunai atau kartu fisik, mendukung sistem pembayaran non-tunai (cashless). Aplikasi dompet digital yang sangat populer saat ini, seperti Dana, OVO, dan Gopay, memiliki banyak pengguna, sehingga sering kali terdapat ulasan yang tidak relevan dengan aplikasi serta rating yang diberikan di Google Play Store. Tujuan penelitian ini adalah untuk membandingkan performa empat algoritma machine learning, yaitu Random Forest, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), dan Naïve Bayes dalam melakukan analisis sentimen pada ulasan aplikasi dompet digital. Data ulasan dompet digital diperoleh melalui teknik data scraping dan selanjutnya dilakukan text preprocessing untuk membersihkan teks agar dapat dieksekusi dengan baik. Hasil penelitian menunjukkan bahwa algoritma Naïve Bayes dan Random Forest memiliki performa terbaik dalam analisis sentimen aplikasi dompet digital. Naïve Bayes mencapai akurasi tertinggi pada aplikasi Gopay dengan nilai 84.44%, recall 84.44%, dan F1-score 82.44%. Sementara itu, Random Forest menunjukkan performa yang konsisten dengan akurasi terbaik pada aplikasi OVO sebesar 81.82% dan recall 81.82%, serta pada aplikasi Gopay dengan akurasi 83.06% dan F1-score 80.84%. Hal ini menunjukkan bahwa kedua algoritma tersebut memiliki potensi yang baik dalam menganalisis sentimen ulasan aplikasi dompet digital
Comparative Performance Analysis of Decision Tree And SVM Algorithms in Detecting Multiple System Atrophy Based on Clinical Features Simatupang, Silvina Enjelia Br; Andreas Nababan; Ruth Agnes E. Tarihoran; Jepri Banjarnahor
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 3 (2025): Article Research July 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i3.15073

Abstract

Multiple System Atrophy (MSA) is a progressive neurodegenerative disorder that presents significant challenges in early and accurate diagnosis. Advances in machine learning algorithms offer promising solutions for improving diagnostic support in medical fields, particularly in complex disorders such as MSA. This study compares the performance of two widely used classification algorithms Decision Tree (DT) and Support Vector Machine (SVM) in detecting MSA using clinical datasets consisting of 300 patient records. Supervised learning techniques with cross-validation were employed, and key performance metrics including accuracy, precision, recall, and F1-score were evaluated. SVM achieved an accuracy of 88.1% and F1-score of 87.1%, outperforming Decision Tree, which recorded 85.4% accuracy and an F1-score of 83.9%. The novelty of this study lies in its direct comparative benchmark using standardized clinical features for MSA detection, offering practical insights into model selection for neurodegenerative disease screening. The SVM model’s superior performance indicates its suitability for reliable early detection of MSA from clinical data. This research contributes to the development of machine learning-based decision support tools in neurology.
Comparative Analysis of Random Forest and Logistic Regression Methods in Predicting Leukemia Blood Cancer Using Microscopic Blood Cell Images Banjarnahor, Jepri; Relungwangi, Galuh Wira
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 3 (2025): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i3.2393

Abstract

Leukemia is one of the deadliest blood cancers that urgently requires early detection for effective treatment. However, conventional diagnosis methods are often subjective, time-consuming, and expensive, posing challenges especially in resource-constrained areas. This study presents a comprehensive comparative analysis of two widely-used machine learning algorithms - Random Forest (RF) and Logistic Regression (LR) - for leukemia prediction using an open-access dataset of 10,661 preprocessed microscopic blood cell images from Kaggle. The dataset was carefully partitioned into training (80%) and testing (20%) sets, with rigorous preprocessing including image normalization and feature extraction. Our evaluation incorporated multiple performance metrics: accuracy, sensitivity, specificity, and AUC. The results show that Random Forest's performance is superior with a classification accuracy of 85.23%, specificity of 0.9351, sensitivity of 0.6774, and AUC of 0.8881, significantly outperforming LR which achieved an accuracy of 78.11%, specificity of 0.8363, sensitivity of 0.6742, and AUC of 0.8120. These findings suggest that ensemble methods like RF are particularly well-suited for detecting one of the most deadly blood cancers, leukemia, due to their ability to handle complex feature interactions in medical imaging data. While both algorithms have potential as clinical decision support, future research can test deep learning techniques and larger datasets to improve the accuracy and reliability of the model.
Co-Authors -, Amalia -, Evta Indra Alfred Army Man Duha Andreas Nababan Aurelia Xinara Lim Barasa, Randy Aldany Bawamenewi, Deskarya Chau, Sugandi Damanik, Ruth Tetra David C. Hutajulu Dikky Irfansyah Dina Pratiwi, Dina Elekda Permata Sari Manurung Fransisca Giawa, Well Friend Gulo, Esthin Mitra Haposan Lumbantoruan Hutagalung, Jessy Putrionom Indra, Evta Ira Monalisa Irfansyah, Dikky JetaJones, Catherine Junita Sari Ninggolan Kasa Lopian Kelvin Kelvin Khosandy, Vincent Kumala, Sinta Lumbantobing, Christian Frederic Mardi Turnip, Mardi Medalsan C Monalisa, Ira Munte, Syahrian Peralla Nainggolan , Dicky Wijaya NK Nababan, Marlince Oloan Sihombing Oloan Sihombing, Oloan Ompusunggu, Elvis Sastra Panjaitan, Mega Lastarida Purba, Windania Rahil, Rafif Reinaldo, Erick Relungwangi, Galuh Wira Ridho, Muhammad Alfathan Ruth Agnes E. Tarihoran Saut Parsaoran Tamba Setiawan, Wendy Shandika , Muhammad Faja Shriram ram Siahaan, Mikael Sianturi, Angelia Chrismeshi Sheila Sihombing , Nissi Grace Dian Simamora, Wanda Pratama Putra Simatupang, Silvina Enjelia Br Sinaga, Wilson Sinta Kumala Sinurat, Stiven Hamonangan Sirait , Janiali Siregar, Regina Sitanggang, Wahyu Adventus Andreas Siti Aisyah Sitorus, Dedi Setiadi Sitorus, Ferdinand Jery Wilkinson Solly Aryza Sri Hartati Sinaga Sugandi Chau Syahrian Peralla Munte Tamba, Saut Parsaoran Tanoto, Carvin Tanzil, Alferedo Unggul Siregar Wibowo, Yonatan Adi Winata, Jaspin Yanmil V. H. Purba Yanto, Willi Yonata Laia Yulianus Zega Zai , Ferman Zuhdi, Muhammad Fikri Akbar