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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 Teknosains : Jurnal Sains,Teknologi dan Informatika 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 Ekonomi, Teknologi dan Bisnis Jurnal Pengabdian Masyarakat VISA: Journal of Vision and Ideas
Aidil Halim Lubis
Universitas Islam Negeri Sumatera Utara
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Kombinasi Algoritma Beaufort Chiper dan Hill Chiper Dalam Mengamankan File Dokumen Berbasis Mobile Siregar, Muhaimi Rizki; Santoso, Heri; Lubis, Aidil Halim
JISTech (Journal of Islamic Science and Technology) Vol 9, No 2 (2024)
Publisher : UIN Sumatera Utara Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/jistech.v9i2.22633

Abstract

Penelitian ini bertujuan untuk mengamankan suatu file dokumen yang memiliki informasi yang bersifat rahasia atau pribadi. Sehingga dibutuhkan suatu sistem yang dapat digunakan untuk mengamankan file dokumen sehingga tidak dapat diakses oleh orang lain yang tidak berkepentingan. Pengamanan file dokumen dilakukan menggunakan kombinasi algoritma beaufort cipher dan hill cipher. Beaufort cipher adalah salah satu varian dari vigenèrecipher di mana cara melakukan enkripsi dan dekripsi hampir sama dengan melakukan enkripsi dan dekripsi pada vigenèrecipher. HillCipher merupakan salah satu algoritma kriptografi yang memanfaatkan matriks sebagai kunci untuk melakukan enkripsi dan dekripsi dari aritmatika modulo. Langkah awal pengamanan dilakukan menggunakan algoritma Beaufort Cipher selanjutnya hasil pengamanan algoritma Beaufort Cipher diamankan kembali menggunakan hill cipher. Proses pengamanan dilakukan menggunakan 4 buah kunci dikarenakan hill cipher menggunakan perkalian matrix 2x2. Hasil akhir dari penelitian ini merupakan sebuah aplikasi berbasis android yang dapat digunakan dalam proses pengamanan file dokumen dengan ekstensi .docx, .xlsx dan .pdf. File dokumen yang telah diamankan akan memliki ekstensi .mhi sebagai penanda bagi peneliti. Dokumen yang telah diamankan dapat dikembalikan ke dalam bentuk aslinya menggunakan aplikasi yang telah dihasilkan pada penelitian ini menggunakan kunci yang sama digunakan dalam proses pengamanan.
Transformation of Binjai Police Presence Application: UI/UX Design with Design Thinking Method to Improve Efficiency and User Experience Algifahri, Muhammad Dzar; Putra, Donny Dwi; Zulfi, Tio Fahreza Zulfi; Lubis, Aidil Halim
Internet of Things and Artificial Intelligence Journal Vol. 4 No. 1 (2024): Volume 4 Issue 1, 2024 [February]
Publisher : Association for Scientific Computing, Electronics, and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/iota.v4i1.690

Abstract

Attendance systems have become integral to attendance management and employee supervision in various organizations. Binjai Resort Police, as a law enforcement agency in the region, is now using an access control system for all its employees. The system is expected to be a new solution for attendance management in the agency, providing efficiency and accuracy in monitoring employee attendance. In today's digital era, attention to user interface (UI/UX) is essential in product development, especially mobile applications. The ultimate goal of this study is to create an attendance mobile application prototype that meets the company's needs. Design Thinking methodology was used to focus on problem-solving by prioritizing end-user needs. The design process consists of five steps: Empathize, Define, Ideate, Prototype and Testing, and Testing. The test results show that the design is already running well, following the needs, and has the potential for further development.
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.
Prediksi Penjualan Salad Buah Kembar Berbasis Produk Homemade Berdasarkan Data Transaksi Menggunakan K-Nearest Neighbor Rahman, Anisa; Hasibuan, Muhammad Siddik; Lubis, Aidil Halim
Jurnal IPTEK Bagi Masyarakat Vol 5 No 1 (2025)
Publisher : Ali Institute of Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/j-ibm.v5i1.1248

Abstract

K-Nearest Neighbor is a method used to classify data with the closest distance or what is called a lazy learning technique. Accurate sales predictions allow owners to plan raw material stock requirements more efficiently, thereby reducing the risk of overstocking (which can cause waste) or understocking (which has the potential to reduce revenue). This research will implement the KNN, K-Nearest Neighbor algorithm to show the extent to which the KNN method can produce accurate predictions in the context of sales. The stages of the method carried out in this study by determining the value of K, calculating the square of the euclid distance (query instance) of each object against the given training data, then sorting the objects into groups that have the smallest euclid distance, using the class label Y (nearest neighbor classification), using the k-nearest neighbor category that is the majority then the calculated query instance value can be predicted. This research produces a jupyter notebook-based sales prediction model that can be used to predict Twin Fruit Salad products based on variations. The dataset used consists of 112 data divided into 89 training data and 23 testing data. With 91% accuracy results, it contributes to increasing the turnover of twin fruit salad and can provide considerations regarding stock availability based on the amount sold.
Penerapan Gestionnaire Libre De Parc Informatique (GLPI) Pada PT. PGAS Telekomunikasi Nusantara (Pgncom) Tasya, Yulianda; Lubis, Aidil Halim
Jurnal Ekonomi Teknologi dan Bisnis (JETBIS) Vol. 1 No. 4 (2022): Jurnal Ekonomi, Teknologi dan Bisnis
Publisher : Al-Makki Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57185/jetbis.v1i4.22

Abstract

Pada awalnya PT. PGas Telekomunikasi Nusantara masih menggunakan cara pencatatan persediaan secara manual. Oleh karena itu, PGNcom beralih ke aplikasi berbasis web. Aplikasi tersebut adalah GLPI yang merupakan database pusat yang mengumpulkan data Agen FusionInventory, dan menyediakan antarmuka web untuk menampilkan dan mencari database inventaris aset. GLPI diterapkan pada PT. PGas Telekomunikasi Nusantara (PGNcom) untuk memudahkan karyawan khususnya di bidang inventory dalam mencatat produk dan melihat perkembangan aset. Flowchart dideskripsikan untuk mengetahui alur dari suatu proses yang diterapkan pada implementasi GLPI.
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.
Co-Authors Abdul Halim Hasugian Adjie Prakasa Viragupty Algifahri, Muhammad Dzar Annisa Aprilia 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 Harahap, Yunus Fadillah Hazizah Ulfa Nasution Husein Nur Ansyari Ilka Zufria 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 Ningrum, Indri Ayu Nurmaiyah, Nurmaiyah Pandu Wicaksono Putra, Donny Dwi Qori Siagian Rahman, Anisa Rahman, Elsa Azila Rakhmat Kurniawan R Ridho Rizky Nasution Rizky Pulungan, Miftahul Santoso, Heri Saragih, Ahmad Fadhly Sani Shella Fahdilla Sari Sihombing, Rizki Andika Siregar, Muhaimi Rizki Siregar, Romadon Goring Sri Wahyuni Sriani Sriani Sriani Suhardi Suhardi, Suhardi Supiyandi Supiyandi Tasya, Yulianda Wahyudi Wahyudi Yunus Fadillah Harahap Yusuf Ramadhan Nasution Zulfi, Tio Fahreza Zulfi