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All Journal Infotech Journal Sinkron : Jurnal dan Penelitian Teknik Informatika JET (Journal of Electrical Technology) 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
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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.
Naïve Bayes and Bidirectional Algorithm Analysis: Encoder Representations From Transformers (BERT) to Teachers' Learning Services to Students Based on the Website of SMK Multi Karya School Sianturi, Ismail; Iqbal, Muhammad; Sitorus, Zulham
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.v9i2.8968

Abstract

This study analyzes the comparison of two algorithms, namely Naive Bayes and Bidirectional Encoder Representations From Transformers (BERT), for the evaluation of the performance of education personnel at SMK MULTI KARYA This study uses manual calculation methods and the Python application. The results showed that the Naive Bayes algorithm gave very consistent results with accuracy, precision, and recall values of 76.67% both in manual calculations and with Pyton. This indicates that the Naive Bayes algorithm is effective in grouping data on the performance of education personnel. Meanwhile, the Bidirectional Encoder Representations From Transformers (BERT) algorithm shows mixed results, while with Python it reaches 12.00%. There are significant differences in recall values and precision between these two calculation methods. Nevertheless, the performance category "Good Performance Staff" remains the most dominant. The difference in results between manual and python calculations is that Naive bayes is a more stable and consistent method across different platforms, whereas Bidirectional Encoder Representations From Transformers (BERT) shows flexibility but with smaller variation in results. Therefore, in the context of education performance evaluation, NAive bayes are more reliable to produce consistent performance categories, while Bidirectional Encoder Representations From Transformers(BERT) can be an alternative with a fairly high level of accuracy but require further consideration in the interpretation of the results..
Analysis of Public Sentiment Towards Tax Increases Impacting Unemployment Using SVM and Multinomial Naive Bayes Methods Haliza, Siti Nur; Sitorus, Zulham; Muhammad Iqbal
JURIKOM (Jurnal Riset Komputer) Vol. 12 No. 4 (2025): Agustus 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i4.8922

Abstract

Tax increase policies often generate pros and cons among the public, especially when perceived as having an impact on increasing unemployment. This study aims to analyze public sentiment regarding the issue of tax increases impacting unemployment by utilizing Machine Learning classification methods, namely Support Vector Machine (SVM) and Multinomial Naive Bayes (MNB). The data used comes from social media platform X in the form of public opinions collected online and then categorized into three sentiments: positive, negative, and neutral, with a total of 1,000 sentiment data points. The analysis process included text preprocessing, feature extraction with TF-IDF, and classification using both methods. In the Test and Score algorithm, the SVM algorithm produced an AUC of 0.660, CA of 0.694, F1 of 0.569, and Recall of 0.694, while the MNB algorithm produced an AUC of 0.586, CA of 0.198, F1 of 0.105, and Recall of 0.198. The study concluded that Support Vector Machines (SVMs) had a higher level of accuracy than Multinominal Naïve Bayes in classifying public sentiment. The majority of public opinion tended to be negative, indicating concern about the impact of tax increases on the workforce. These findings provide important insights for policymakers to consider public perception when establishing future fiscal policy.
ANALYSIS OF THE LEVEL OF EFFECTIVENESS OF THE INDEPENDENT CAMPUS MERDEKA LEARNING PROGRAM (MBKM) USING METHODSPREFERENCE SELECTION INDEX (PSI) AND VIKOR METHOD Kiki Artika; Muhammad Iqbal; Zulham Sitorus; Andysah Putera Utama Siahaan; Rian Farta Wijaya
Bulletin of Engineering Science, Technology and Industry Vol. 2 No. 3 (2024): September
Publisher : PT. Radja Intercontinental Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59733/besti.v2i3.61

Abstract

This research aims to analyze the level of effectiveness of the Independent Campus Learning Program (MBKM) using the Preference Selection Index (PSI) Method and VIKOR Method. The MBKM program is an initiative of the Ministry of Education and Culture of the Republic of Indonesia which aims to provide more flexibility and learning opportunities for students through various off-campus activities. This research was conducted to measure the extent to which the program succeeded in achieving its goals. The PSI method is used to determine preferences for various aspects of the program based on assessments from students and academic staff, while the VIKOR method is used to identify the best compromise solution that can maximize stakeholder satisfaction. Analysis was carried out to assess the effectiveness of the program based on several criteria, including the quality of the learning experience, relevance to the world of work, and contribution to student skills development. This research suggests that to further increase the effectiveness of the MBKM Program, there needs to be an emphasis on developing a curriculum that is more responsive to industry needs and improving supporting facilities for students. The implications of the results of this research are important for policy makers in designing educational strategies that are more adaptive and oriented to labor market needs.
MACHINE LEARNING ANALYSIS IN IMPROVING THE EFFICIENCY OF THE STUDENT ADMISSION DECISION MAKING PROCESS NEW AT PANCA BUDI MEDAN DEVELOPMENT UNIVERSITY M. Rasyid; Zulham Sitorus; Rian Farta Wijaya; Muhammad Iqbal; Khairul
Bulletin of Engineering Science, Technology and Industry Vol. 2 No. 3 (2024): September
Publisher : PT. Radja Intercontinental Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59733/besti.v2i3.62

Abstract

The decision-making process in admitting new students is a crucial aspect that can influence the quality and efficiency of academic administration in higher education. This research aims to analyze the role of Machine Learning methods, especially Support Vector Machines (SVM), in increasing the efficiency of the decision-making process for new student admissions at the Panca Budi Development University, Medan. The data used in this research includes information from the student admissions process for the odd semester of the 2022/2023 academic year, which includes various variables such as Registration Number, School of Origin, Registration Payment, and others. The data is divided into a training set (70%) and a testing set (30%). The Support Vector Machine (SVM) model that was built was evaluated using metrics such as accuracy, precision, recall, and F1-Score. The research results show that the SVM model achieves an accuracy of 100%, with high precision and recall for both classes. Precision for both classes reached 1.00, while recall for the minority class (class 1) reached 0.91, indicating excellent model performance in classification. The conclusion of this research is that the Support Vector Machine (SVM) model can significantly increase efficiency and accuracy in the decision-making process for new student admissions at the Panca Budi Development University in Medan compared to conventional methods. These findings indicate that the application of Machine Learning methods can provide substantial benefits in the context of academic administration.
ANALYSIS OF GOOGLE USER SENTIMENT TOWARDS UNIVERSITAS PEMBANGUNAN PANCA BUDI BASED ON REVIEWS GOOGLEUSING THE NAÏVE BAYES ALGORITHM M Imam Santoso; Rian Farta Wijaya; Zulham Sitorus; Muhammad Iqbal; Leni Marlina
Bulletin of Engineering Science, Technology and Industry Vol. 2 No. 3 (2024): September
Publisher : PT. Radja Intercontinental Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59733/besti.v2i3.63

Abstract

This thesis examines user sentiment towards Panca Budi Development University by utilizing Google reviews as the main data and using the Naïve Bayes algorithm for sentiment analysis. This research aims to understand the public's perception of the university through reviewing reviews available on the Google platform. The data used consists of user reviews collected from Google Reviews. The analysis process begins with data pre-processing, including text cleaning and tokenization, followed by the development of a Naïve Bayes model for classification of review sentiment into positive, negative, or neutral categories. The results of this analysis provide insight into the strengths and weaknesses of Panca Budi Development University from a user perspective, as well as identifying areas that require improvement. It is hoped that these findings can become a basis for the university to improve the quality of its services and reputation in the eyes of the public. This research also highlights the effectiveness of the Naïve Bayes algorithm in sentiment analysis, and contributes to further studies on sentiment analysis in the education sector
Analisa Classification Decision Tree C45 dan Naïve Bayes Pada Indikasi Penyakit Diabetes Menggunakan Rapid Miner Hamzah, Iswadi; Zulham Sitorus; Khairul
Jurnal Nasional Teknologi Komputer Vol 4 No 1 (2024): Volume 4 Nomor 1 Januari 2024
Publisher : CV. Hawari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61306/jnastek.v4i1.126

Abstract

In Indonesia, the rate of diabetes sufferers continues to increase, so this is deemed necessary to pay attention to by the Indonesian people in particular, for this reason this research is not the first to be conducted. Predicting diabetes can be done using various methods through various algorithms which are quite diverse, therefore it is necessary to conduct research on the algorithms used. To obtain new information, the Decision Tree algorithm with Naïve Bayes was tested using the Rapid Miner application. This test is carried out on data that has the attribute HighBP, HighChol, CholCheck, BMI, Smoker, Stroke, Heart Diseaseor Attack, Phys Activity, Fruits, Veggies, HvyAlcoholConsump, AnyHealthcare, NoDocbcCos, GenHlth, MentHlth, PhysHlth, DiffWalk, Sex, Age, Education, Income. All of these attributes serve as a guide in determining results, so that it can be known that the patient has diabetes.
Co-Authors , Arpan , Fery Anugerah , Rahima Br Purba A.A. Ketut Agung Cahyawan W Abda Abda Ade Surya Bakti Pane Afrizal, Henri Afrizal, Sandi Aldi Kesuma Alvian Alvian Ami Abdul Jabar Amnisuhaila Abarahan Ananda Aulia Andi Ernawati 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 Izhari Fahmi Kurniawan Farta wijaya, Rian Feby Wulandari Sembirinng Fikri Zuhaili Simbolon Gilang Ramadhan Gultom, Ananda Christianto Hafiz Rodhiy Haliza, Siti Nur Hamzah, Iswadi Harmiati Bungsu Bangun Hartono Sinambela, Sugi Helmy, Ahmad Hendra Harnanda Heni Wulandari Hilal Prayogi Hrp, Abdul Chaidir Ibezato Zalukhu, Anzas Ika Devi Perwitasari Indra Angkat, Chairul IQBAL , MUHAMMAD Irwan Syahputra Irwan Syahputra, Irwan 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 Hafizh Al-Ghifari Rangkuti Muhammad Iqbal Muhammad Irfan Sarif Muhammad Syahputra Novelan 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 Ragil Satya Adi W Rahmat Hidayat Ramadani, Pebri Ramadhan, Aditya Ramadhani, Aditya Rangga Rafandi 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 Septia Harliansyah Septia Harliansyah Septiani, Nadya Sianturi, Ismail Sibarani, Dina Marsauli Simamora, Siska 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 Solly Aryza 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 Yasri, Afif Zai, Yulianus Zalukhu, Anzas Ibezato Zulfahmi Syahputera Zulfahmi Syahputra Zulfahmi Zulfahmi Zulfahmi Zulfahmi