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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
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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.
Penerapan Algoritma Certainty Factor dalam Diagnosa Penyakit Guillain-Barre Syndrome Indra Angkat, Chairul; Marzuki Sianturi, Ismail; Hartono Sinambela, Sugi; Sitorus, Zulham; Khairul, Khairul
Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) Vol. 7 No. 1 (2024): Jurnal Ilmu Komputer dan Sistem Informasi
Publisher : Utility Project Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jikomsi.v7i1.2609

Abstract

Gangguan neurologis, seperti Guillain-Barre Syndrome (GBS), terjadi akibat disfungsi otak atau sistem saraf manusia, dengan dampak yang dapat merugikan pertumbuhan anak dari segi psikologis dan fisik. Gejala bervariasi tergantung pada bagian otak atau sistem saraf yang terpengaruh, terjadi pada berbagai rentang usia, mulai dari bayi hingga usia dewasa. GBS, penyakit sistem saraf tepi, dapat muncul pada berbagai kelompok usia dan disebabkan oleh peradangan yang merusak lapisan mielin saraf pada motor neuron serta melibatkan kelainan autoimun pada beberapa individu yang terkena. Infeksi terkait GBS dapat disebabkan oleh bakteri Campylobacter pylori yang dapat dideteksi melalui pemeriksaan laboratorium. Meskipun sangat langka, hanya sekitar 1 dari 100.000 orang yang mengalami gangguan sistem saraf akibat penyakit ini. Gejala GBS mencakup kram otot, parestesia, kesulitan menelan, kesulitan bernafas, kehilangan respons motorik, peningkatan denyut nadi, gangguan pencernaan, kelebihan keringat, dan ketidakstabilan tekanan darah. Untuk memfasilitasi diagnosa penyakit ini, disarankan penggunaan sistem pakar yang dapat memberikan keputusan melalui analisis masalah dengan menerapkan metode atau algoritma tertentu, termasuk algoritma certainty factor. Hal ini diharapkan dapat meningkatkan efisiensi dan objektivitas dalam proses diagnosa penyakit GBS tanpa memerlukan pertemuan langsung antara dokter dan pasien. Penelitian ini bertujuan untuk mengidentifikasi jenis penyakit berdasarkan gejala atau keluhan pasien melalui penerapan certainty factor. Hasil diagnosa menunjukkan persentase tingkat keyakinan sebesar 99.7%, memberikan kontribusi dalam penyediaan diagnosis yang cepat, efisien, dan objektif, mengurangi ketergantungan pada pertemuan langsung antara dokter atau pakar dengan pasien.
Implementasi Sistem Arsip Elektronik Dalam Meningkatkan Efisiensi Operasional Di Smk Gelora Jaya Nusantara Medan Simbolon, Fikri Zuhaili; Izhari, Fahmi; Sitorus, Zulham
Jurnal Minfo Polgan Vol. 13 No. 2 (2024): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v13i2.14157

Abstract

Penelitian ini bertujuan untuk menganalisis implementasi sistem arsip elektronik dalam meningkatkan efisiensi operasional di SMK Gelora Jaya Nusantara Medan. Sistem arsip elektronik merupakan teknologi yang menggantikan metode penyimpanan dokumen konvensional berbasis kertas dengan format digital yang lebih efisien dan mudah diakses. Implementasi sistem ini diharapkan dapat mengurangi waktu dan biaya yang diperlukan untuk mencari dan mengakses dokumen, meningkatkan keamanan data, serta mendukung inisiatif ramah lingkungan dengan mengurangi penggunaan kertas. Penelitian ini menggunakan metode kualitatif dengan pendekatan studi kasus untuk mengidentifikasi manfaat dan tantangan yang dihadapi dalam penerapan sistem arsip elektronik di lingkungan sekolah. Hasil penelitian menunjukkan bahwa implementasi sistem arsip elektronik di SMK Gelora Jaya Nusantara Medan berhasil meningkatkan efisiensi operasional, mengurangi biaya penyimpanan fisik, dan meningkatkan aksesibilitas serta keamanan dokumen. Namun, terdapat beberapa tantangan seperti kebutuhan pelatihan staf dan pemeliharaan sistem yang perlu diperhatikan untuk memastikan keberlanjutan dan efektivitas sistem.
Analysis of Public Sentiment Towards Tax Increases Impacting Unemployment Using SVM and Multinomial Naive Bayes Methods Haliza, Siti Nur; Sitorus, Zulham; Muhammad Iqbal
JURNAL RISET KOMPUTER (JURIKOM) 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.
Analisis Metode FMEA Dan SPC Pada Proyeksi Losses Produksi Dan Prediksi Perawatan Pompa Minyak Di PT.Pertamina Ep Zona 1 Rantau Field Kuala Simpang Sugito, Bambang; Iqbal, Muhammad; Sitorus, Zulham
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 10, No 1 (2025): Edisi Februari
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v10i1.889

Abstract

Leaks in oil pumping systems are among the main causes of production losses and reduced operational efficiency in the upstream oil and gas industry. This study aims to identify dominant failure modes, monitor process stability, and predict pump leakage risk using an integrated approach that combines Failure Mode and Effect Analysis (FMEA), Statistical Process Control (SPC), and the Support Vector Machine (SVM) algorithm. The research was conducted at PT Pertamina EP Zone 1 Rantau Field using operational data from the year 2024. FMEA results show that leakage due to illegal tapping and corrosion are the most critical failures, with Risk Priority Numbers (RPN) of 216 and 180, respectively. SPC analysis using X̄-R control charts revealed weekly process fluctuations, indicating potential variability in operations. To predict leakage risk, an SVM model was trained using technical pump features such as pressure, temperature, vibration, and pipe joint age. Class imbalance was addressed using the Synthetic Minority Oversampling Technique (SMOTE), and model evaluation yielded an accuracy of 95.48%. The integration of these three methods has proven effective in supporting data-driven predictive maintenance strategies. It reduces unplanned downtime, minimizes production losses, and enhances the reliability of oil pumping systems.
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