Article Per Year (5 Year)
p-Index From 2021 - 2026
9.52
P-Index
This Author published in this journals
All Journal Infotech Journal Sinkron : Jurnal dan Penelitian Teknik Informatika IT JOURNAL RESEARCH AND DEVELOPMENT INTECOMS: Journal of Information Technology and Computer Science KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) The IJICS (International Journal of Informatics and Computer Science) JURIKOM (Jurnal Riset Komputer) JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Teknik dan Informatika Building of Informatics, Technology and Science Jurnal Mantik Jurnal Sains dan Teknologi Community Engagement and Emergence Journal (CEEJ) Jurnal Tekinkom (Teknik Informasi dan Komputer) Jatilima : Jurnal Multimedia Dan Teknologi Informasi Journal of Computer System and Informatics (JoSYC) INFOKUM Jurnal Darma Agung Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) International Journal Of Science, Technology & Management (IJSTM) Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Media Informatika Instal : Jurnal Komputer Jurnal Info Sains : Informatika dan Sains Bulletin of Information Technology (BIT) International Journal of Social Science, Educational, Economics, Agriculture Research, and Technology (IJSET) Jurnal Fokus Manajemen Jurnal Minfo Polgan (JMP) Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Jurnal Nasional Teknologi Komputer Jurnal Pengabdian Masyarakat Gemilang (JPMG) Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Data Sciences Indonesia (DSI) DEVICE : JOURNAL OF INFORMATION SYSTEM, COMPUTER SCIENCE AND INFORMATION TECHNOLOGY Best Journal of Administration and Management Bulletin of Engineering Science, Technology and Industry
Sitorus, Zulham
UNIVERSITAS PEMBANGUNAN PANCA BUDI
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

Published : 82 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 82 Documents
Search

 4   5   6   7   8 
Designing an Alumni Information System Based on UML (Unified Modeling Language) Sitorus, Zulham; Pranoto, Sugeng; Sutiono, Sulis; Arief, Muhammad
Bahasa Indonesia Vol 15 No 02 (2023): Instal : Jurnal Komputer Periode (Juli-Desember)
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jurnalkomputer.v15i02.162

Abstract

This research designed a UML-based Alumni Information System for SD IT Jabal Nur Tebing Tinggi. The stages involve collecting data through observation and interviews, followed by designing UML using use case, activity, sequence, and class diagrams. The aim is to facilitate collaboration between schools and system developers, providing a visual depiction of admin and system interactions in managing alumni data. Although still at the design stage, the results provide a strong basis for further implementation, with the potential to increase efficiency and support future school development. The suggestion involves a system implementation phase to test the effectiveness of the design, accompanied by clear documentation for system maintenance and new user training.
Pengembangan Dan Implementasi Aplikasi Pelayanan Jasa Berbasis Web Pada CV Altrama Energi Gultom, Ananda Christianto; Batubara, Supina; Sitorus, Zulham
Data Sciences Indonesia (DSI) Vol. 4 No. 2 (2024): Article Research Volume 4 Issue 2, December 2024
Publisher : Yayasan Cita Cendikiawan Al Kharizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/dsi.v4i2.5401

Abstract

Kualitas jasa layanan yang prima mampu menciptakan loyalitas konsumen sehingga dapat tercipta hubungan kerja yang saling menguntungkan antara kedua belah pihak. CV Altrama Energi adalah salah satu perusahaan layanan jasa penyewaan alat berat maupun perawatan atau perbaikannya untuk berbagai kebutuhan pekerjaan berat seperti konstruksi maupun pertanian. Dengan kondisi pengelolaan data yang tidak efektif mengakibatkan pimpinan CV Altrama Energi kesulitan melakukan pencarian data, pemantauan jadwal pekerjaan maupun penagihan. Hal ini dapat mempengaruhi reputasi CV Altrama Energi dimata konsumen yang dapat mengakibatkan terputusnya hubungan kerjasama dengan konsumen. Tujuan penelitian ini adalah mengembangkan dan mengimplementasikan aplikasi layanan jasa berbasis web untuk mengolah data jasa penyewaan dan jasa perbaikan alat berat pada CV Altrama Energi menggunakan metode prototipe, perancangan aplikasi menggunakan unified modelling language, dan pengujian menggunakan metode Black Box. Hasil penelitian ini menunjukkan bahwa metode prototipe cocok digunakan dalam pengembangan aplikasi di CV Altrama Energi karena seringnya terjadi perubahan kebutuhan serta aplikasi yang dihasilkan user friendly digunakan oleh pegawai administrasi maupun pimpinan CV Altrama Energi.
Penerapan Data Mining Untuk Klasifikasi Penduduk Miskin Di Kabupaten Labuhanbatu Menggunakan Random Forest Dan K-Nearest Neighbors Ernawati, Andi; Khairul; Sitorus, Zulham; Iqbal, Muhammad; Nasution, Darmeli
Bulletin of Information Technology (BIT) Vol 6 No 2: Juni 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i1.1783

Abstract

This study aims to apply and compare the performance of two data mining algorithms—Random Forest (RF) and K-Nearest Neighbors (KNN)—in classifying poverty status among residents of Labuhanbatu Regency. The dataset includes information on occupation, income, housing, and education from 21,137 individuals. After undergoing preprocessing, model training, hyperparameter optimization, and evaluation, both models were assessed using five key metrics: accuracy, precision, recall, F1-score, and AUC. The results show that Random Forest performed slightly better than KNN, achieving an accuracy of 0.6023, precision of 0.4827, recall of 0.4177, F1-score of 0.4479, and an AUC of 0.5681. In comparison, KNN obtained an accuracy of 0.5990, precision of 0.4771, recall of 0.4006, F1-score of 0.4355, and an AUC of 0.5622. Based on these findings, it can be concluded that Random Forest is more effective for poverty classification on this dataset, although the performance difference is relatively small.
Analisis Sentimen Penerapan Deep Learning dan Analisis Sentimen terhadap Gap Kompetensi Lulusan Lembaga Pendidikan dan Pelatihan Vokasi terhadap Dunia Kerja dengan Metode Long Short-Term Memory (LSTM) Yahya, Susilawati; Sitorus, Zulham; Iqbal, Muhammad; Nasution, Darmeli; Farta Wijaya, Rian
Bulletin of Information Technology (BIT) Vol 6 No 2: Juni 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i2.2031

Abstract

The gap between vocational graduates’ competencies and labor market demands remains a pressing issue in Indonesia. This study aims to analyze alumni perceptions regarding the alignment between competencies acquired during their studies at LP3I Banda Aceh and real-world job requirements. A quantitative approach was adopted using a deep learning method based on Long Short-Term Memory (LSTM). Data were collected through an online survey containing open-ended responses from 934 alumni, followed by preprocessing, tokenization, lexicon-based sentiment labeling, and data splitting into training and testing sets. The models developed included pure LSTM, LSTM with class weights, and Bidirectional LSTM (BiLSTM). Results indicate that BiLSTM achieved the highest performance with 90% accuracy and a weighted F1-score of 0.91. Additionally, 44.5% of respondents expressed neutral or negative sentiments, highlighting a mismatch between acquired competencies and industry demands. These findings underscore the urgency of curriculum evaluation and stronger collaboration between vocational institutions and the labor market. This study demonstrates that deep learning offers an efficient and objective tool for competency mapping in vocational education.
Perancangan Sistem Administrasi Pada Kantor Lurah Kuala Langkat Berbasi Web Sitepu, Fernando; Sitorus, Zulham; Perwitasari, Ika Devi
DEVICE : JOURNAL OF INFORMATION SYSTEM, COMPUTER SCIENCE AND INFORMATION TECHNOLOGY Vol 6, No 1: JUNI 2025
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/device.v6i1.7320

Abstract

Studi ini bertujuan untuk merancang sistem manajemen berbasis web yang dapat meningkatkan efisiensi dan efektivitas pelayanan di Kantor Lurah Kuala Langkat. Sistem ini diciptakan untuk menyederhanakan proses pengelolaan data administrasi, seperti pendaftaran, pengelolaan surat, dan pengarsipan dokumen secara digital. Metodologi yang diterapkan dalam penelitian ini adalah metode pengembangan perangkat lunak dengan pendekatan terstruktur, yang mencakup analisis kebutuhan, desain, pembangunan, dan pengujian sistem. Dalam perancangan sistem, digunakan bahasa pemrograman PHP dan MySQL sebagai basis data, dengan tujuan untuk mempermudah pengelolaan data secara daring dan memastikan aksesibilitas yang lebih baik bagi pegawai dan masyarakat. Hasil dari studi ini diharapkan dapat memberikan solusi praktis untuk mengurangi pemakaian kertas, mempercepat proses administrasi, serta meningkatkan akurasi dan keamanan data di Kantor Lurah Kuala Langkat. Dengan adanya sistem manajemen berbasis web ini, diharapkan pelayanan publik di tingkat kelurahan dapat menjadi lebih transparan, efisien, dan terorganisir
Analisis Sentimen Google Review terhadap Mutu Kualitas Pendidikan pada Perguruan Tinggi STIE Al-Washliyah Sibolga dengan Metode Lexicon dan Algoritma Naive Bayes Tanjung, Miftah Rusydi; Iqbal, Muhammad; Sitorus, Zulham
Jurnal Multimedia dan Teknologi Informasi (Jatilima) Vol. 7 No. 02 (2025): Jatilima : Jurnal Multimedia Dan Teknologi Informasi
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jatilima.v7i02.1549

Abstract

Perkembangan teknologi informasi telah mendorong masyarakat untuk menyampaikan opini terhadap institusi pendidikan melalui platform digital seperti Google Review. Penelitian ini bertujuan untuk menganalisis sentimen masyarakat terhadap mutu layanan pendidikan di STIE Al-Washliyah Sibolga berdasarkan komentar Google Review dengan membandingkan dua pendekatan analisis sentimen, yaitu Lexicon-Based dan Naïve Bayes. Sebanyak 51 komentar dianalisis melalui tahapan praproses teks yang meliputi case folding, tokenisasi, stopwords removal, dan stemming. Hasil klasifikasi menunjukkan bahwa sebagian besar komentar bersentimen positif, dengan fokus pada kenyamanan lingkungan kampus, keramahan layanan, serta aksesibilitas lokasi. Model dievaluasi menggunakan metrik akurasi, presisi, recall, dan F1-score. Hasil evaluasi menunjukkan bahwa metode Naïve Bayes memiliki performa lebih unggul dengan akurasi sebesar 98%, recall 97,8%, dan F1-score 98,8%, dibandingkan Lexicon-Based yang hanya mencapai akurasi 94,1% dan F1-score 96,6%. Temuan ini menunjukkan bahwa citra STIE Al-Washliyah Sibolga di ruang digital sangat positif, serta algoritma Naïve Bayes layak digunakan sebagai pendekatan efektif dalam pemantauan opini publik terhadap perguruan tinggi berbasis data digital.
Artificial Intelligence Analysis of Recommendations for Granting Business Licenses to Determine the Priority of Business Supervision and Control Using the DBSCAN Method (Case Study: DPMPTSP Langkat Regency) diansyah, Suhar; Sitorus, Zulham; Iqbal, Muhammad
The IJICS (International Journal of Informatics and Computer Science) Vol. 9 No. 2 (2025): July
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/ijics.v9i2.8900

Abstract

In facing the challenges of limited resources and business complexity, the Investment and One-Stop Integrated Services Office (DPMPTSP) of Langkat Regency requires a data-driven approach to determine priorities for business supervision and enforcement. This study applies the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm to cluster business entities based on three main parameters: risk level, business scale, and licensing status. Secondary data from 3,748 companies were collected, processed through label encoding and normalization, and analyzed in a three-dimensional space (X1_Risk, X2_Scale, X3_License). The clustering results revealed the formation of clusters and a Silhouette Score value, indicating optimal cluster structure and separation between groups. Each cluster was interpreted as a representation of recommendation categories such as Routine Monitoring and Evaluation, Intensive Monitoring and Evaluation, Administrative Warning, Temporary Operational Suspension, and Permanent Operational Termination. The resulting visualizations enhanced the understanding of spatial mapping and clustering patterns comprehensively. This demonstrates that DBSCAN is effective as a decision-support tool for automated and objective priority mapping in business supervision, and capable of detecting business entities that deviate from general norms (outliers). This approach significantly contributes to improving the efficiency and accuracy of decision-making in business license supervision and enforcement at the regional level.
ROI and SNA Analysis in Testing the Effectiveness of New Student Admission Promotion: A Case Study at MAS Al Washliyah Gedung Johor Angkat, Chairul Indra; Sitorus, Zulham; Iqbal, Muhammad
The IJICS (International Journal of Informatics and Computer Science) Vol. 9 No. 2 (2025): July
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/ijics.v9i2.8901

Abstract

Globalization and intense competition in the education sector, especially among private high schools, require institutions such as MAS Al Washliyah Gedung Johor to continue optimizing their new student admission promotion strategies. Although the school has implemented multi-channel promotions that include social media (Instagram, TikTok), conventional methods (brochures), and financial incentives (alumni tuition fee discounts), there has been no in-depth analysis of the effectiveness of each variable. The problem of less than optimal promotion results due to inappropriate media selection often results in inefficient allocation of promotion costs with minimal student recruitment results. This study aims to analyze the effectiveness of various promotion variables used by MAS Al Washliyah Gedung Johor, in order to support a more appropriate and efficient allocation of funding sources. Data were collected through a questionnaire given to new students regarding their sources of promotional information. To achieve this goal, this study uses a two-method approach: Return on Investment (ROI) to measure financial efficiency and return on funds, and Social Network Analysis (SNA) to visualize interaction patterns, reach, and identify the most influential communities or promotions in the student exposure network. By combining ROI and SNA analysis, it is hoped that this study can provide clear information regarding promotion costs and the most efficient and effective types of promotion, as a basis for improving the school promotion system in the future.
Performance Analysis of CNN (Convolutional Neural Network) in Nominal Classification of Rupiah Emissions 2022 Sahputra, Fajar; Sitorus, Zulham; Iqbal, Muhammad; Marlina, Leni; Nasution, Darmeli
The IJICS (International Journal of Informatics and Computer Science) Vol. 9 No. 2 (2025): July
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/ijics.v9i2.8903

Abstract

This study aims to analyze the performance of Convolutional Neural Network (CNN) algorithm in classifying the nominal of Rupiah banknotes issued in 2022. Three test models are developed, namely two CNN architectures with different optimizers (Adam and RMSprop), and one transfer learning model using VGG16. The dataset used consists of 1,848 banknote images of seven denominations: Rp1,000, Rp2,000, Rp5,000, Rp10,000, Rp20,000, Rp50,000, and Rp100,000. The data was collected using a smartphone camera and processed through augmentation, normalization, and classification stages. The model was evaluated using accuracy, precision, recall, and F1-score metrics. The results show that CNN with Adam's optimizer achieves a validation accuracy of 98.97%, while CNN with RMSprop reaches 99.59%. Meanwhile, the VGG16 model achieved perfect validation accuracy of 100%, with precision, recall, and F1-score values of 1.00 each. These results show that the transfer learning approach provides the best performance compared to conventional CNN models. This research supports the development of an accurate and efficient banknote recognition automation system for digital finance applications.
Comparative Analysis of Sequencing Methods and Markov Models for Predicting High-Achieving Students at Budi Darma University Sinambela, Sugi Hartono; Iqbal, Muhammad; Khairul, Khairul; Darmeli Nasution; Zulham Sitorus
The IJICS (International Journal of Informatics and Computer Science) Vol. 9 No. 2 (2025): July
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/ijics.v9i2.8964

Abstract

The prediction of high-achieving students is a strategic step in supporting the development of academic quality within higher education institutions. This study aims to compare two data mining approaches, namely the Sequencing method and the Markov Model, in predicting high-achieving students at Universitas Budi Darma Medan. The Sequencing method is used to identify patterns in the sequence of academic grades and non-academic activities of students from semester to semester, while the Markov Model is used to calculate the probability of transitions in students' academic status based on historical data. The research adopts a quantitative approach involving 100 active students with complete academic and non-academic data. The data analyzed include semester GPA, participation in organizations, seminars, and achievements in competitions. Both methods were evaluated using metrics such as accuracy, precision, recall, and F1-score. The evaluation results show that the Sequencing method achieved an accuracy of 87%, precision of 85%, recall of 88%, and an F1-score of 86%, while the Markov Model recorded an accuracy of 81%, precision of 79%, recall of 83%, and an F1-score of 81%. Based on these results, the Sequencing method is considered superior in detecting patterns and providing more accurate predictions of students’ achievement potential. The comparison of these two methods provides a foundation for institutions to develop more accurate, objective, and comprehensive student achievement prediction systems. Thus, universities can implement early and well-targeted interventions and guidance.
Co-Authors , Arpan , Fery Anugerah , Rahima Br Purba A.A. Ketut Agung Cahyawan W Abda Abda Ade Surya Bakti Pane Afrizal, Sandi Akbar Maulana, Taufik Aldi Kesuma Alvian Alvian Ami Abdul Jabar Amnisuhaila Abarahan Andi Ernawati Andysah Putera Utama Siahaan Angkat, Chairul Indra Anshari, Ari Antoni, Robin Ardya, Dwika Arief, Muhammad Arif Rahman Astri Mutia Rahma Audry, Beby Aulia, Ananda Ayu Ofta Azhari, M. Idrus azwan, m Baehaqi Bambang Sugito Batubara, Supina Boy Rizki Akbar Br Tarigan, Sella Monika Chelfina Utami Daniel Happy Putra Danu Wardhana Azhari Darmeli Nasution DEWI SARTIKA diansyah, Suhar Diva, Krisna Eko Hariyanto Eko Hariyanto Eko Hariyanto Eko Wahyudi Erbin Sitorus Fachri, Barany Fahmi Iskandar Fahmi Kurniawan Farta wijaya, Rian Faza Wardanu Damanik, Dwi Feby Wulandari Sembirinng Gilang Ramadhan Gultom, Ananda Christianto Hafiz Rodhiy Haliza, Siti Nur Hamzah, Iswadi Harmiati Bungsu Bangun Hartono Sinambela, Sugi Helmy, Ahmad Hendra Harnanda Heni Wulandari Hrp, Abdul Chaidir Ibezato Zalukhu, Anzas Ika Devi Perwitasari Indra Angkat, Chairul IQBAL , MUHAMMAD Irwan Syahputra Irwan Syahputra, Irwan Izhari, Fahmi Khairul Khairul Khairul, Khairul Kiki Artika Kurniawan, Fahmi Laila Maghfirah Larius Ambasador Parlindungan Leni Marlina Leni Marlina Lia Nazliana Nasution Limbong, Yohannes France M Imam Santoso M. Rasyid M.Rizki Khadafi Mardiah, Nia Marzuki Sianturi, Ismail Maulian Saputra Melva Sari Panjaitan Meri Sri Wahyuni Mhd Arie Akbar Mhd Ihsan Abidi Mohammad Yusuf, Mohammad Muhammad Fahriza Muhammad Iqbal Muhammad Irfan Sarif Muhammad Wahyudi Nahampun, Natalia Nainggolan, Andreas Ghanneson Nainggolan, Irfan Nazar Saputra, Risfan Nelviony Parhusip Nurwijayanti Ofta Sari, Ayu Parhusip, Nelviony Pasaribu, Ryan Fahreza Pranoto, Sugeng Putra, Khairil Rafandi, Rangga Ragil Satya Adi W Rahmat Hidayat Ramadani, Pebri Ramadhan, Aditya Ramadhan, Deni Ramadhani, Aditya Razaq, Abdul Retno Mutiara Rian Farta Wijaya Rian Putra, Randi Rika Uli Samosir, Siska Risky, Raihan Rowiyah Asengbaramae Rusydi Tanjung , Miftah Sahputra, Fajar Said Oktaviandi Sari Penjaitan, Melva Septia Harliansyah Septiani, Nadya Sianturi, Ismail Sibarani, Dina Marsauli Simamora, Siska Simbolon, Fikri Zuhaili Simorangkir, Elsya Sabrina Asmita Sinambela, Sugi Hartono Sinyo Andika Nasution, Ahmad Sipra Barutu Siregar, Andree Risky Yuliansyah Sitepu, Fernando Siti Nurhaliza Sofyan Sitinur, Siti Nurhaliza Sofyan Sitompul, Jelly Rolley Sofyan, Siti Nurhaliza Solly Ariza Lubis Sri Wahyuni, Meri Suhardiansyah Suhardiansyah Suhardiansyah Suherman Suherman Sukrianto, Sukrianto Sutiono, Sulis Syahputri, Maulisa Syamsiar, Syamsiar T, Siti Isna Syahri Tanjung, Miftah Rusydi Tiara Aninditha Tumangger, Oktavia Utama, Hendra Vina Arnita Vivin Yulfia Sarah Wahyu Agung Pratama Wahyuni, Meri Sri Wijaya, Rian Farta Wirda Fitriani Yahya, Susilawati Zai, Yulianus Zalukhu, Anzas Ibezato Zulfahmi Syahputera Zulfahmi Zulfahmi Zulfahmi Zulfahmi