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All Journal Elkom: Jurnal Elektronika dan Komputer CESS (Journal of Computer Engineering, System and Science) Sinkron : Jurnal dan Penelitian Teknik Informatika JISTech (Journal of Islamic Science and Technology) IJISTECH (International Journal Of Information System & Technology) JURTEKSI JOURNAL OF SCIENCE AND SOCIAL RESEARCH Simtek : Jurnal Sistem Informasi dan Teknik Komputer Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Technologia: Jurnal Ilmiah TIN: TERAPAN INFORMATIKA NUSANTARA Journal of Computer Networks, Architecture and High Performance Computing Journal of Intelligent Computing and Health Informatics (JICHI) Jurnal Teknologi Informasi dan Komunikasi IJISTECH Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Journal of Dinda : Data Science, Information Technology, and Data Analytics Jurnal IPTEK Bagi Masyarakat Journal of Computer Science and Informatics Engineering Jurnal Ilmu Komputer dan Teknologi (IKOMTI) Jurnal Informatika Teknologi dan Sains (Jinteks) Sistem Pendukung Keputusan dengan Aplikasi DEVICE : JOURNAL OF INFORMATION SYSTEM, COMPUTER SCIENCE AND INFORMATION TECHNOLOGY Internet of Things and Artificial Intelligence Journal Jurnal Pengabdian Masyarakat VISA: Journal of Vision and Ideas Algebra : Jurnal Pendidikan Sosial dan Sains TEKNOSAINS: Jurnal Sains, Teknologi dan Informatika
Aidil Halim Lubis
Universitas Islam Negeri Sumatera Utara
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Klasifikasi Pengaruh Negatif Game online Bagi Remaja Menggunakan Algoritma Naïve Bayes Siregar, Romadon Goring; Lubis, Aidil Halim; Ikhsan, Muhammad
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.v6i1.6655

Abstract

Penelitian ini bertujuan untuk mengklasifikasikan pengaruh negatif game online bagi remaja menggunakan algoritma Naïve Bayes. Data diperoleh dari 265 siswa/siswi SMA Negeri 2 Padang Bolak dan diklasifikasikan ke dalam tiga tingkat: Ringan, Sedang, dan Parah. Sebanyak 250 data digunakan untuk pelatihan dan 15 data untuk pengujian. Hasil pengujian menunjukkan bahwa 12 dari 15 data berhasil diklasifikasikan dengan benar (akurasi 80%). Precision dan recall tertinggi terdapat pada kelas Ringan dan Sedang, sementara kelas Parah tidak terdeteksi. Naïve Bayes efektif untuk klasifikasi ringan dan sedang, namun perlu perbaikan untuk kelas parah.
Public Complaints Application at Binjai City Police Using the Waterfall Method to Improve the Performance of Binjai District Police Dimas, Dimas; Lubis, Aidil Halim
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.v4i3.1130

Abstract

The web-based public complaints application in Binjai City aims to improve the effectiveness and transparency of police services. The system allows the public to submit reports online, monitor the status of complaints, and communicate more efficiently with civil servants. With features such as real-time notifications, police database reporting, and integration, the app speeds up responses to symptoms and improves the accountability of the local police in Binjai. The use of this technology also reduces manual bureaucracy, speeds up case solutions, and increases public trust in the police. System testing shows that the app can optimize complaint workflows and provide more responsive solutions. Therefore, this app can be an innovative model in modernizing police services in the digital era.
Sentiment Analysis on TikTok Discourse Surrounding the 2024 North Sumatra Gubernatorial Election Using Support Vector Machine Algorithm Istiqomah, Istiqomah; Lubis, Aidil Halim
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 3 (2025): Articles Research July 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i3.6549

Abstract

This study aims to analyze public sentiment towards the 2024 North Sumatra gubernatorial election by leveraging social media data, specifically TikTok, which has become a major platform for political discourse in Indonesia. The two competing candidate pairs, Bobby Nasution–Surya and Edy Rahmayadi–Hasan Basri, have sparked widespread online discussions that range from enthusiastic support to harsh criticism. These interactions have a significant impact on public opinion formation and may influence electoral outcomes. To address this phenomenon, this research implements a sentiment classification model using the Support Vector Machine (SVM) algorithm with a polynomial kernel, known for its effectiveness in handling high-dimensional textual data. A total of 2,100 TikTok comments were collected using scraping techniques via Python. The data then underwent several preprocessing stages, including case folding, cleaning, normalization, tokenizing, slangword removal, stopword removal, and stemming. Feature extraction was conducted using the TF-IDF method, followed by lexicon-based sentiment labeling into positive and negative classes. The classification model achieved an accuracy of 82%, with a positive sentiment precision of 0.81, recall of 0.96, and F1-score of 0.88. For negative sentiment, the precision was 0.86, recall 0.51, and F1-score 0.64. These findings indicate that the model performs well in identifying explicit positive sentiments but faces challenges in recognizing complex negative expressions such as sarcasm or implicit criticism. The results provide valuable insights into digital political behavior and demonstrate the potential of machine learning-based sentiment analysis as a tool for monitoring public perception in real time during elections.
Analysis of Public Sentiment Toward the Increase in VAT Rates Using the SVM Algorithm Rahman, Elsa Azila; Lubis, Aidil Halim
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 5 No 2 (2025): August
Publisher : Research Group of Data Engineering, Faculty of Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/dinda.v5i2.2025

Abstract

The Policy Of Increasing the Value Added Tax (VAT), particularly on luxury goods as stipulated in Minister of Finance Regulation (PMK) Number 131 of 2024, has sparked various public responses, many of which are captured through social media. In today's digital era, social media has become a primary platform for the public to express their opinions openly, including on government policies. This study aims to analyze public sentiment toward the VAT policy in order to provide insights for more responsive policymaking. A total of 4,000 comments were collected from the X platform using web crawling techniques, followed by preprocessing, resulting in 3,553 clean comments. Sentiment labeling was conducted automatically using a lexicon-based approach, which revealed that the majority of comments expressed positive sentiment (73.3%), while the remainder were negative (26.7%). Sentiment classification was performed using the Support Vector Machine (SVM) algorithm with a polynomial kernel and an 80:20 training-testing data split. Evaluation results showed that the model achieved an accuracy of 76.65%. The SVM model demonstrated excellent performance in detecting positive sentiment (precision 76.18%, recall 100%, and F1-score 86.51%), but was less effective in identifying negative sentiment (precision 100%, recall 7.78%, and F1-score 14.44%). These findings indicate that while the model is effective in recognizing positive opinions, further optimization is needed to improve performance in detecting negative sentiments.
Sentiment Analysis of Public Opinion on Facebook Monetization in Social Media Using the SVM Algorithm Nurmaiyah, Nurmaiyah; Lubis, Aidil Halim
TIN: Terapan Informatika Nusantara Vol 6 No 3 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i3.8210

Abstract

Sentiment analysis on Facebook’s monetization policy has become a significant topic in the era of rapid digital transformation. This study examines public opinion on the policy by analyzing TikTok user comments that specifically discuss Facebook monetization. TikTok was chosen as the data source because it reflects spontaneous and real-time public reactions, including discussions about other platform policies. A total of 5,000 TikTok comments were collected using web scraping techniques. The data underwent several preprocessing stages, including text cleaning, tokenization, normalization, stopword removal, and stemming. Sentiment labeling was carried out using the Indonesian Sentiment Lexicon (InSet), while feature extraction employed the Term Frequency–Inverse Document Frequency (TF-IDF) method. The classification process was conducted using the Support Vector Machine (SVM) algorithm with a linear kernel. The dataset was split into training and testing sets with an 80:20 ratio. The classification achieved an accuracy of 80%, with a precision of 80% for both positive and negative sentiments, recall scores of 81% and 79%, and F1-scores of 81% and 79%, respectively. These findings demonstrate that integrating TF-IDF weighting with the SVM algorithm is effective for automatically classifying public sentiment toward social media monetization policies. Furthermore, this study provides insights into public reactions to Facebook monetization from the perspective of TikTok users, thereby contributing to an understanding of how monetization policies influence user sentiment on social media platforms.
Sistem Deteksi Kecenderungan Perilaku Agresif Akibat Pengaruh Smartphone Terhadap Psikologis Anak Menggunakan Metode Teorema Bayes dan Certainty Factor Wahyudi, Wahyudi; Zufria, Ilka; Lubis, Aidil Halim
TIN: Terapan Informatika Nusantara Vol 4 No 12 (2024): May 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v4i12.5112

Abstract

The ease of obtaining information and having complete functions in one hand makes an individual tend not to be separated from a smartphone even for a while because the individual has felt dependent on the smartphone function. Excessive smartphone use in children can have a negative impact on their psychological health, especially in terms of aggressive behavior. Excessive use of smartphones can make children more impulsive, irritable, and prone to aggressive actions. Therefore, a system is needed that can detect the tendency of aggressive behavior in children due to the influence of smartphones to prevent the encouragement of aggressive behavior in children that can make these children capable of committing criminal acts and other negative actions using an expert system. The methods used in this research are the certainty factor method and the Bayes theorem method. Bayes theorem is used to classify and calculate the probability value of a child's tendency to aggressive behavior due to the influence of smartphones. While the certainty factor method is used to determine the confidence value of the probability value obtained using the Bayes theorem. The results of this study illustrate the feasibility of the proposed framework in correctly recognizing the tendency of coercive behavior influenced by smartphone use among children. By utilizing Bayes' theorem and certainty factor, it is expected that this research can help in conducting early detection of the level of aggressive behavior tendencies due to the influence of smartphones. Based on the research that has been done, the system successfully detects 3 (three) levels of tendency, namely low, medium, and high with a percentage of 100% with the results of the Bayes theorem and certainty factor calculations showing in class P3 with a combination of CFcombine(CFold_4,CF_13) has a percentage of 94.17% confidence level and judging from the results of the calculation of the certainty factor combination formula above, it can be concluded that, the child has a tendency to High aggressive behavior.
Implementasi Algortima Support Vector Machine Dalam Klasifikasi Komentar Pengguna Produk Skintific di E-Commerce Artika, Priti Rindi; Lubis, Aidil Halim
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 14, No 2: Agustus 2025
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v14i2.3137

Abstract

The large number of consumer reviews of skincare products such as Skintific 5X Ceramide Barrier Repair Moisture Gel on e-commerce platforms raises the need for automated sentiment analysis. This study classifies 2,000 user comments from the Sociolla app using the Support Vector Machine (SVM) algorithm. Data were obtained through web scraping and processed through preprocessing, lexicon-based labeling, and word weighting using TF-IDF. SVM with a linear kernel was used to distinguish positive and negative comments. Performance evaluation using a confusion matrix resulted in an accuracy of 89.73%, a precision of 0.94, a recall of 0.75, and an F1-score of 0.83 for the positive class, and a precision of 0.88, a recall of 0.98, and an F1-score of 0.93 for the negative class. These results indicate that SVM is effective for sentiment classification in online beauty product reviews.Keywords: E-commerce; Support Vector Machine; Sentiment Analysis; TF-IDF; Sociolla; Skintific; Lexicon-based AbstrakBanyaknya ulasan konsumen terhadap produk perawatan kulit seperti Skintific 5X Ceramide Barrier Repair Moisture Gel di platform e-commerce menimbulkan kebutuhan akan analisis sentimen otomatis. Penelitian ini mengklasifikasikan 2000 komentar pengguna dari aplikasi Sociolla menggunakan algoritma Support Vector Machine (SVM). Data diperoleh melalui web scraping dan diproses dengan tahapan preprocessing, pelabelan berbasis lexicon, serta pembobotan kata menggunakan TF-IDF. SVM dengan linear kernel digunakan untuk membedakan komentar positif dan negatif. Evaluasi performa menggunakan confusion matrix menghasilkan akurasi sebesar 89,73%, precision 0,94, recall 0,75, dan F1-score 0,83 untuk kelas positif, serta precision 0,88, recall 0,98, dan F1-score 0,93 untuk kelas negatif. Hasil ini menunjukkan bahwa SVM efektif untuk klasifikasi sentimen pada ulasan produk kecantikan secara daring. 
Penerapan Metode Simple Additive Weighting pada Sistem Pendukung Keputusan Pemilihan Raket Bulu Tangkis Saragih, Ahmad Fadhly Sani; Muhammad Ikhsan; Lubis, Aidil Halim
Jurnal IT UHB Vol 6 No 3 (2025): Jurnal Ilmu Komputer dan Teknologi
Publisher : Universitas Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/ikomti.v6i3.1995

Abstract

Badminton is one of the most popular sports in Indonesia and a source of national pride due to the achievements of its athletes in international tournaments. The racket is the primary equipment used in badminton, and selecting the appropriate one is crucial for performance and comfort. However, many beginner players still find it difficult to choose a suitable racket according to the coach’s recommendation. Therefore, this study developed a Decision Support System (DSS) for badminton racket selection by applying the Simple Additive Weighting (SAW) method. Four alternatives—Yonex, Li-Ning, Flypower, and Victor—were evaluated using five criteria: price, racket weight, head type, shaft flexibility, and handle size. The integration of the SAW method into a web-based application produced ranking results for racket selection. The results showed that Li-Ning G Force Superlite 3900 achieved the highest score of 0.8845, indicating it as the most suitable racket for beginner players.
Penerapan Metode PID pada Sistem Pemberi Pakan Kucing Otomatis Berbasis IOT (Internet of Things) Fadiga, Muhammad; Kurniawan, Rakhmat; Lubis, Aidil Halim
VISA: Journal of Vision and Ideas Vol. 5 No. 1 (2025): Journal of Vision and Ideas (VISA)
Publisher : IAI Nasional Laa Roiba Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47467/visa.v5i1.5772

Abstract

An automatic feeding system that can be monitored and managed remotely is needed amidst the busy schedules of pet owners, especially for cats. As a result, an automatic cat feeder that can be operated and viewed remotely via the Internet of Things was developed in this research. By understanding the properties of loadcell sensors and servo motors, research in this case seeks to create an automatic cat feeding system that uses a PID approach to regulate food according to the cat's needs. For the convenience of remote use, the system also allows monitoring and control using the Telegram application. An ESP32 microcontroller, a servo for food release, a loadcell sensor for food weight measurement, and a camera for cat detection were all used in the development of this research system. An automatic cat feeding system that produces servo motor and load cell sensor calibration values with average error results of 1.84%, 3.86%, and accuracy of 96.14%, 98.16% was successfully developed in this research. With an error percentage of 1.67%, this research was able to produce food that matched the cat food dosage using the PID approach with trial and error tuning, especially with a cat food dosage of 60 grams. Based on the research findings, it can be said that this system works as planned and offers a practical way to feed cats food precisely and automatically.
Analisis Sentimen Masyarakat Terhadap Resesi Ekonomi Global 2023 Menggunakan Algoritma Naïve Bayes Classifier Sriani; Lubis, Aidil Halim; Harahap, Yunus Fadillah
Elkom: Jurnal Elektronika dan Komputer Vol. 16 No. 2 (2023): Desember : Jurnal Elektronika dan Komputer
Publisher : STEKOM PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/elkom.v16i2.1673

Abstract

The global economic recession is a global economic downturn that affects the domestic economies of countries in the world. The stronger the economic dependence of one country on the global economy, the faster a recession will occur in that country. In 2020 the country of Indonesia and even the world are exposed to the COVID-19 virus which has an impact on the country's economic growth, even the world economy. This is the trigger for an economic recession. This has led to many different public perspectives on the occurrence of a global economic recession whose opinions or reactions are expressed on social media Youtube. The data was obtained by crawling techniques from social media Youtube with a total of 500 comments used. The data is then labeled (class) with a lexicon-based method with an Indonesian language dictionary. From the labeling results, it was obtained 185 positive labeled data (37%) and 315 negative opinions (63%). The data preprocessing stage is carried out in preparation for the data to be processed for sentiment analysis. Of the many opinions obtained, an analysis of public sentiment regarding the 2023 global economic recession will be carried out using the Naïve Bayes classification algorithm. This study also applied the TF-IDF word weighting method with the n-gram feature used, namely bigram (n=1). The system will be evaluated using a confusion matrix. The implementation results show a prediction model with a total of 500 opinion data with a comparison of training data and test data of 9:1, producing an accuracy value of 84.00%, a precision value of 75.00%, a recall of 30.00%, and an f1-score of 42.86%. The performance of the system model built in this study can be said to be good.
Co-Authors Abdul Halim Hasugian Adjie Prakasa Viragupty Algifahri, Muhammad Dzar Anisa Rahman Annisa Aprilia Armansyah Armansyah Armansyah Artika, Priti Rindi As’Ary Sahlul Irsyad Darisma Daulay Desmi Roma Putra Lubis Dimas, Dimas Erma Julita, Erma Fadiga, Muhammad Faris Jihad Galang Desdwiansyah Hakim, Yusrizal Harahap, Yunus Fadillah Hazizah Ulfa Nasution Husein Nur Ansyari Ilka Zufria Indri Ayu Ningrum istiqomah istiqomah Jihan Ulfa Deski Kiki Widya Pratiwi Lia Putri Ashari Lubis M Alif Fahrezy Mhd Furqan Monica Situmeang Muhammad Alwi Baihaqi Muhammad Ihsan Nugraha Muhammad Ikhsan Muhammad Ikhsan Muhammad Siddik Hasibuan Muhammad Zarlis, Muhammad Nurmaiyah, Nurmaiyah Pandu Wicaksono Putra, Donny Dwi Qori Siagian Rahman, Elsa Azila Rakhmat Kurniawan R Ridho Rizky Nasution Rizki Ali Akbar Pasaribu Rizky Pulungan, Miftahul Santoso, Heri Saragih, Ahmad Fadhly Sani Shella Fahdilla Sari Sihombing, Rizki Andika Siregar, Muhaimi Rizki Siregar, Rizki Abdul Hakim Siregar, Romadon Goring Sitorus, Dhafa Hibrizi Sri Wahyuni Sriani Sriani Sriani Suhardi, Suhardi Supiyandi Supiyandi Wahyudi Wahyudi Yunus Fadillah Harahap Yusuf Ramadhan Nasution Zulfi, Tio Fahreza Zulfi