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All Journal Teknika Techno.Com: Jurnal Teknologi Informasi Jurnal Informatika PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnal Teknologi Informasi dan Ilmu Komputer CESS (Journal of Computer Engineering, System and Science) JURNAL PENGABDIAN KEPADA MASYARAKAT Jurnal Ilmiah KOMPUTASI Sistemasi: Jurnal Sistem Informasi JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING IT JOURNAL RESEARCH AND DEVELOPMENT Indonesian Journal of Artificial Intelligence and Data Mining JRST (Jurnal Riset Sains dan Teknologi) Jurnal Teknovasi : Jurnal Teknik dan Inovasi Mesin Otomotif, Komputer, Industri dan Elektronika JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jusikom : Jurnal Sistem Komputer Musirawas ALGORITMA : JURNAL ILMU KOMPUTER DAN INFORMATIKA JISTech (Journal of Islamic Science and Technology) Jurnal Teknologi Sistem Informasi dan Aplikasi JSiI (Jurnal Sistem Informasi) IJISTECH (International Journal Of Information System & Technology) Journal on Education JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Teknik Elektro dan Komputer TRIAC Jurnal Riset Informatika INFOMATEK: Jurnal Informatika, Manajemen dan Teknologi METIK JURNAL Jurnal Ilmiah Ilmu Komputer Fakultas Ilmu Komputer Universitas Al Asyariah Mandar Jurnal Mantik Journal of Information Systems and Informatics INFOKUM U-NET Jurnal Teknik Informatika Journal of Computer Networks, Architecture and High Performance Computing Jurnal Teknologi Informasi dan Komunikasi Jurnal Teknik Informatika (JUTIF) IJISTECH Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Computer Science and Information Technology (CoSciTech) International Journal of Artificial Intelligence and Robotics (IJAIR) Jurnal Pendidikan dan Teknologi Indonesia Journal La Multiapp KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Info Sains : Informatika dan Sains Jurnal IPTEK Bagi Masyarakat Journal of Computer Science and Informatics Engineering Journal Of Human And Education (JAHE) Prisma Sains: Jurnal Pengkajian Ilmu dan Pembelajaran Matematika dan IPA IKIP Mataram International Conference on Sciences Development and Technology STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer Innovative: Journal Of Social Science Research Jurnal Pengabdian Masyarakat VISA: Journal of Vision and Ideas Cosmic Jurnal Teknik
Muhammad Siddik Hasibuan
Universitas Islam Negeri Sumatera Utara
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 Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Other

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Pendekatan Metode DBSCAN dan Fuzzy C-Means untuk Klasterisasi Skala Prioritas Stunting Lubis, Putri Natasya; Hasibuan, Muhammad Siddik
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 2 (2025): JPTI - Februari 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.655

Abstract

Stunting merupakan permasalahan kesehatan yang berdampak pada perkembangan fisik dan kognitif anak, serta berkontribusi terhadap kesenjangan sosial dan ekonomi di Indonesia. Penelitian ini bertujuan untuk mengembangkan metode klasterisasi skala prioritas penanganan stunting dengan menggabungkan algoritma DBSCAN (Density-Based Spatial Clustering of Applications with Noise) dan Fuzzy C-Means (FCM). Data diperoleh dari platform Aksi Bangda Kemendagri (2021–2024) dan diolah menggunakan Python. DBSCAN digunakan untuk mengidentifikasi wilayah dengan konsentrasi tinggi kasus stunting dan mendeteksi outlier, sementara FCM membantu menentukan prioritas intervensi berdasarkan tingkat keparahan. Hasil penelitian menunjukkan bahwa DBSCAN menghasilkan dua klaster utama dan sejumlah outlier, sedangkan FCM membagi data menjadi tiga klaster berbasis derajat keanggotaan. Pendekatan ini berpotensi menjadi alat analitik dalam mendukung kebijakan percepatan penurunan angka stunting secara lebih efektif di Indonesia.
Sentiment Analysis on Application X on the Use of Red Oil Using the Naïve Bayes Method Tanjung, Tajuddin; Hasibuan, Muhammad Siddik
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 1 (2025): March 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i1.35422

Abstract

Red oil, as an alternative to traditional cooking oil, has gained public attention through reviews on App X. However, questions arise about how public sentiment is towards red oil and how the Naïve Bayes algorithm can classify positive and negative sentiments. This study aims to analyze user sentiment towards red oil using the Naïve Bayes method. The dataset used consists of 1,200 comments collected through the scrapping technique in 2024. After going through the process of removing duplicate comments, the number of data becomes 1,189. Before running the Naïve Bayes algorithm, the data is divided into test data and training data, with 238 data as test data and 951 data as training data. The analysis process involves pre-processing stages such as text cleaning, tokenization, and normalization, followed by word weighting with the TF-IDF method. The Naïve Bayes algorithm is applied for the classification of positive and negative sentiments. The results showed that 1,147 comments were positive sentiment, while 42 comments were negative sentiment with a total accuracy of 88.66%, then precision of 95.41%, recall of 92.44% and F1- 93.91% and it was found that the sentiment comments on the use of red oil had a greater positive polarity than negative polarity. This analysis provides important insights for producers and stakeholders regarding public perception of red oil, which is useful for strategic decision making, such as improving product quality and marketing campaigns. This method is expected to be a reference for further studies in the field of text classification and natural language processing.
Analysis of Covid-19 Vaccine Policy Sentiment Using SVM and C4.5 Hasibuan, Muhammad Siddik; suhardi, suhardi
Jurnal Teknik Elektro dan Komputer TRIAC Vol 9, No 2 (2022): Special Edition
Publisher : Jurusan Teknik Elektro Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/triac.v9i2.14781

Abstract

Analisis sentimen terhadap kebijakan vaksin covid-19 di Indonesia menjadi suatu hal yang perlu untuk diteliti. Opini tersebut dapat di jadikan suatu model penelitian, yaitu menggunakan metode klasifikasi data mining menggunakan algortima SVM dan C4.5. Dataset yang digunakan adalah opini atau sentimen masyarakat yang di posting pada media sosial twitter. Data yang diambil sebanyak 200 data, selanjutnya dilakukan proses pre-prosessing menggunakan metode TD-IDF data menjadi 137 dateset. Proses selanjutnya menyeimbangkan data dengan fungsi SMOTE, hasil dari performance dari algoritma SVM mendapat nilai akurasi 99.46 sedangkan algoritma C4.5 mendapat nilai akurasi 69.02. Dari hasil analisis yang dilakukan algoritma SVM mendapat nilai optimum yang lebih baik dari algoritma C4.5.
Analisis Perbandingan Silhouette dengan Elbow pada Algoritma K-Means dan DBSCAN Khairani Ritonga, Putri; Siddik Hasibuan, Muhammad
METIK JURNAL (AKREDITASI SINTA 3) Vol. 9 No. 1 (2025): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/metik.v9i1.1027

Abstract

This study aims to apply clustering methods using the K-Means and DBSCAN algorithms to group community data based on parameters such as income, housing condition, occupation, number of dependents, and health status. To determine the optimal number of clusters in the K-Means algorithm, the Elbow and Silhouette methods were employed. The research utilized Python and Google Colaboratory as data analysis tools. The clustering results showed that the DBSCAN algorithm was more effective in identifying homogeneous community groups without the need to predefine the number of clusters, while K-Means produced more structured results but relied on a predetermined cluster count. This research is expected to aid in more accurate and efficient decision-making for community data grouping.
Comparison of Naïve Bayes and Dempster Shafer Algorithms for the Diagnosis of ARI Diseases Haikal, Baginda Fikri; Hasibuan, Muhammad Siddik; Rifki, Mhd Ikhsan
Journal of Computer Science and Informatics Engineering Vol 4 No 3 (2025): July
Publisher : Ali Institute of Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/cosie.v4i3.1161

Abstract

Acute Respiratory Infection (ARI) has a high prevalence in Indonesia, but the manual diagnosis process faces challenges such as limited medical personnel and uncertainty in symptom analysis. This study developed and compared two AI methods, namely Naïve Bayes and Dempster-Shafer, in a web-based expert system to diagnose ARI. Symptom and disease data were collected from literature and experts, then implemented in a PHP and MySQL-based system. Naïve Bayes was used for probability-based classification, while Dempster-Shafer handled uncertainty. Testing was conducted on one case of ARI. Naïve Bayes produced a probability of 21.99% for Pneumonia, while Dempster-Shafer provided a combined probability of 61.6% for five diseases, including Colds, Acute Pharyngitis, and Epiglottitis. The results show that Naïve Bayes is suitable for consistent single diagnoses, while Dempster-Shafer is more appropriate for conditions with overlapping symptoms and uncertain data
SISTEM PAKAR DIAGNOSIS PENYAKIT PADA BEBEK MENGGUNAKAN METODE FORWARD CHAINING DAN CERTAINTY FACTOR Marpaung, Devi Aryani; Suendri; Muhammad Siddik Hasibuan
JURNAL ILMU KOMPUTER Vol 10 No 1 (2024): Edisi April
Publisher : LPPM Universitas Al Asyariah Mandar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35329/jiik.v10i1.299

Abstract

Ducks are livestock that are kept by many people. Statistics show that Indonesia consistently produces more ducks each year. The rise in duck production indicates a rise in the use and consumption of ducks. Due to the large population of ducks, both communicable and non-communicable diseases are frequently quite prevalent. Breeders of ducks are affected by this, so they need information about duck diseases from an expert. In order to identify the disease being experienced before it progresses to a more serious stage, an expert system application is required to diagnose disease based on symptoms. The goal of this system is to mimic an expert system's knowledge and problem-solving abilities. The certainty factor and forward chaining techniques were applied in the creation of this expert system. The data search procedure in the forward chaining method begins at the premise and ends at the conclusion. Furthermore, the certainty factor is applied to quantify certainty. According to the study's findings, duck diseases were divided into eighteen categories by gathering thirty symptoms and assigning a confidence level to each one between 0 and 1. Based on this choice, the name of the duck disease and its percentage level will be shown.
Detecting Data Leakage in Cloud Storage Using Decision Tree Classification Harahap, Parlindungan; Hasibuan, Muhammad Siddik
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1215

Abstract

Data leakage in cloud storage systems poses a significant security threat, potentially leading to unauthorized access, loss of sensitive information, and operational disruptions. This research proposes a classification model for detecting potential data leakage incidents using the Decision Tree algorithm. The dataset, obtained from the Kaggle public repository, contains user activity logs representing both normal and anomalous behaviors in cloud storage environments. Several preprocessing steps were applied to improve model quality, including handling missing values, removing outliers, and converting categorical data into numerical form. Hyperparameter optimization was performed using GridSearchCV to determine the best configuration for the Decision Tree classifier. Experimental results demonstrate that the optimized model achieved high classification performance, with an accuracy of 70,84%, a precision of 55% for the data leakage class, and an F1-score of 40%. The analysis also highlights the significance of certain features, such as multi-factor authentication usage and access to confidential data, in predicting potential leakage events. This study provides a theoretical contribution by \establishing a robust methodology for applying Decision Tree algorithms to a novel cloud security dataset, offering a scalable and interpretable framework for automated threat detection.
Analisis Respon Anak Remaja Terhadap Perilaku Catcalling Di Desa Mangkai Baru Lorena, Ayu; Selian, Suci Nadillah; Aulia, Dhinanda; Hasibuan, Muhammad Siddik
Journal Of Human And Education (JAHE) Vol. 4 No. 5 (2024): Journal of Human And Education (JAHE)
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jh.v4i5.1527

Abstract

Penelitian ini bertujuan untuk merinci aspek-aspek yang terkait dengan catcalling dengan menggali lebih dalam aspek hukum,etika, dan respon masyarakat. Dalam konteks hukum, studi ini mengidentifikasi relevansi Undang-Undang Pelecehan Seksual. Hasilnya menunjukkan bahwa tindakan tersebut mungkin melanggar peraturan hukum yang ada, yang bertujuan untuk melindungi seseorang dari perlakuan tidak menyenangkan. Analisis juga mencakup pertimbangan hukuman yang dapat diberlakukan dalam kasus semacam ini. Kajian etika dalam penelitian ini menggambarkan konflik nilai-nilai yang muncul seiring dengan kasus tersebut. Catcalling adalah tindakan mengeluarkan komentar atau perilaku yang bersifat seksual atau merendahkan terhadap seseorang di tempat umum, terutama dilakukan oleh seseorang yang tidak dikenal, dengan tujuan untuk memperoleh perhatian atau membuat seseorang merasa tidak nyaman. Catcalling sering melibatkan komentar yang tidak diinginkan terkait penampilan fisik seseorang, seperti pujian yang tidak pantas atau bahkan ucapan yang mengintimidasi atau melecehkan. Ini adalah bentuk pelecehan verbal yang sering kali membuat targetnya merasa terancam atau tidak aman.Etika perilaku terhadap seseorang menjadi fokus utama, dengan perbuatan tidak menyenangkan terhadap seseorang menjadi pelanggaran terhadap norma-norma etika yang melarang perlakuan tidak pantas terhadap seseorang. Selain itu studi ini juga mengulas respon masyarakat yang luar biasa besar terhadap catcalling ini. Aksi protes dan dukungan publik yang luas menunjukkan tingkat kesadaran yang meningkat tentang isu catcalling dimasyarakat.
Pendekatan Bayes-HDSS dalam Menentukan Status Pantauan Gizi Balita Hisbullah, Riki; Hasibuan, Muhammad Siddik
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 5: Oktober 2023
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2023107437

Abstract

Teknologi dan perkembangan komputer saat ini sangat membantu banyak kebutuhan dalam kehidupan, termasuk dalam hal menggantikan kemampuan seorang ahli dalam mengerjakan dan menentukan sebuah keputusan dalam permasalahan yang terjadi pada banyak orang. Dalam perkembangan sistem pakar dan sistem pendukung keputusan juga dapat menggantikan dan menjadi solusi dari seorang ahli pakar. Bayes digunakan untuk mendiagnosis penyakit serta AHP dan TOPSIS akan digunakan untuk melakukan perangkingan penyakit pada manusia, termasuk gizi pada anak dimana sampai saat ini masalah terbesar dunia adalah bagaimana mengatasi gizi buruk yang dialami. Dengan menggunakan Sistem Pakar dan HDSS diharap Mampu menghitung kriteria yang merupakan gejala pada anak guna menghasilkan urutan anak yang menjadi fokus pantauan dimana nilai terbesar dari output sistem memberikan kepastian bahwa anak membutuhkan fokus dalam mengatasi masalah gizi yang dialaminya. Didalam Penelitian ini, Bayes, AHP dan TOPSIS mampu menghitung nilai dengan memberikan diagnosis gejala serta menempilkan nilai persentase kebutuhan pantauan pada data sampel balita yang di peroleh dari Pusat Kesehatan Masyarakat (Puskesmas) XYZ terhadap masalah gizi balita yang ada di Pusat Kesehatan Masyarakat tersebut. Dari 5 Gejala Yang di implementasikan kedalam sistem pakar (bayes) yaitu; STUNTING, GIZI LEBIH, GIZI KURANG, KWASHIORKOR, dan MARASMUS, tidak ditemukan status STUNTING. Selanjutnya pada HDSS Menghasilkan persentase 95,49% yang mana balita ini merupakan pemilik kriteria terburuk diantara balita lainnya.   Abstract Technology and the development of computers are very helpful for many people in life, including in terms of replacing the ability of an expert to do and determine a decision in problems that occur to many people. In the development of expert systems and support systems, decisions can also replace and be a solution to the scarcity of an expert. Bayes is used to diagnose disease and AHP and TOPSIS will be used to rank diseases in humans, including nutrition in children where until now the world's biggest problem is how to overcome malnutrition experienced by toddlers including stunting problems, where toddler growth  is not optimal. By using an Expert System and HDSS (namely AHP and TOPSIS) it is forbidden to calculate criteria that are symptoms in children, to produce a sequence of children who are the focus of monitoring. The greatest value of the output system provides certainty that children need focus in overcoming the problem of malnutrition. Deepened by this research, Bayes, AHP and TOPSIS were able to calculate the value by providing a diagnosis of symptoms and displaying the percentage value of the need for monitoring on the toddler sample data obtained from the XYZ Community Health Center (Puskesmas) for toddler nutrition problems in the Community Health Center. Of the 5 Symptoms implemented in the expert system (bayes) namely STUNTING, OVER NUTRITION, MALNUTRITION, KWASHIORKOR, and MARASMUS, no STUNTING status was found. Next on HDSS Produces a percentage of 95.49% of which this toddler is the owner of the worst criteria among other toddlers.
Classification of Batu Bara Songket Using Gray-Level Co-Occurrence Matrix and Support Vector Machine Sriani, Sriani; Hasibuan, Muhammad Siddik; Ananda, Rizkika
Jurnal Riset Informatika Vol. 5 No. 1 (2022): December 2022
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (839.78 KB) | DOI: 10.34288/jri.v5i1.178

Abstract

Songket is a traditional woven cloth from the Melayu and Minangkabau tribes. Songket can also be classified from the brocade woven family and woven with gold or silver thread. Songket cloth's beauty is the Indonesian people's wealth and preservation. Batu Bara Regency is one of Indonesia's regions with several Songket motifs characteristics. Public knowledge of Batu Bara Songket motifs is still minimal, and the differences between one motif and another are still unknown. This research provides information about the variety of Songket fabrics by classifying six types of Batu Bara Songket motifs, namely the Bunga Tanjung motif, Pucuk Betikam motif, Pucuk Cempaka motif, Pucuk pandan motif, Tampuk Manggis motif and Tolab Berantai motif based on the extraction of the Gray Level texture feature. The Co-Occurrence Matrix includes four parameters: Contrast, Correlation, Energy, and Homogeneity, as well as a classification method with a Support Vector Machine. The feature extraction values ​​ process as input for classification using a Support Vector Machine. The highest accuracy achieved in this study was 57%, using 60 training data and 30 test data.
Co-Authors Abdul Halim Hasugian Ahmad Affandi Rasyad Nasution Ahmad al-Badawi, Abdullah Aidil Halim Lubis Aidil Halim Lubis Ali Darta Ananda, Rizkika Andi Andi Anisa Rahman Anisa Simanjuntak Armansyah Asti, Dini Aulia Nurhasanah, Dhea Aulia, Dhinanda Aulia, M. Arif Bela Sapitri Br Sembiring, Trisna Amanda Dicky Adityanta Sinuraya Efendi, Ayu Mahriza Agustin Erwin Nasution Fadhli Rizqi Haidar Pane Fatih Muhammad, Aji Haikal, Baginda Fikri Hamzah, Aldiva Handira, Dysa Harahap, Parlindungan Harahap, Raihan Hasibuan, Bunga Lestari Heri Santoso Hisbullah, Riki Hotmaidah Harahap Hutabarat, Dio Wahyu Habibi Ichsan Rafisyah Ilka Zufria Indah Permata Sari Ivan Prayuda Khairani Ritonga, Putri Kurniawan, Riski Askia Lestari, Rika Dinda Lipantri Mashur Gultom Lorena, Ayu Lubis, Muhammad Taufik Hakim Lubis, Putri Natasya Mahdiania, Diania Marpaung, Devi Aryani Mhd Furqan Mhd Ikhsan Rifki Mitha Rosadi Mrg, Ricky Aulia Muhammad Abi Muzaki Muhammad Dedi Irawan Muhammad Fadiga Muhammad Ikhsan Muhammad Zulfahmi Nasution Mukhairi Rizal, Muhammad Nasution, Yusuf Ramadhan Naufal, Rahmad Nazhifa Ahmad Fauzan Piramida, Piramida Pratama, Dian Agus Rahmat Kurniawan Rahmat Kurniawan R Rakhmat Kurniawan R Ramadhan, Rizky Syahrul Rangkuti, M. Naufal Reza Adhitya Budiman Riska Hasibuan Rosadi, Mitha Sandira, Sri Delwis Selian, Suci Nadillah Serdano, Akbar Sholihin, Sazili Siagian, Qori Azmi Ayasy Sinuraya, Dicky Adityanta Siregar, Putri Aprilia Sita Kirana Atikah Siti Nurhaliza Sofyan Sri Wahyuni Sriani Sriani Suendri Suhardi Suhardi Suhardi Suhardi, Suhardi Supiyandi Supiyandi Syahputra, Surya Syahputri, Cindy Novi Syaqila, Saidatus Tanjung, Tajuddin Tarigan, Mayang Safhira Triase Triase, Triase Utomo, Imam Yudhistira, Yudhistira Yusuf Karim Rambe Yusuf Ramadhan Nasution Yusuf Ramadhan Nasution, Yusuf Ramadhan