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ALGORITMA : JURNAL ILMU KOMPUTER DAN INFORMATIKA
Published by Universitas Islam Negeri Sumatera Utara
ISSN : -     EISSN : 25986341     DOI : 10.30829/algoritma
Core Subject : Science,
Computer Science & IT
Arjuna Subject : -
Articles 181 Documents
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Efek Peltier Dalam Termoelektrik Sebagai Solusi Alternatif Teknologi Pendingin Berbasis Mikrokontroler ATmega328 Ikhsan, Muhammad
Algoritma: Jurnal Ilmu Komputer dan Informatika Vol 2, No 1 (2018): April 2018
Publisher : Universitas Islam Negeri Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/algoritma.v2i1.28647

Abstract

Teknologi pendingin konvensional umumnya menggunakan kompresor yang memakan tempat dan kurang fleksibel untuk kebutuhan portabel. Penelitian ini bertujuan merancang lemari pendingin pintar (smart refrigerator) mini yang hemat energi dan ramah lingkungan menggunakan efek Peltier sebagai alternatif pengganti kompresor. Metode yang digunakan adalah eksperimen dengan merancang perangkat keras berbasis mikrokontroler ATmega328 dan menerapkan metode Pulse Width Modulation (PWM) untuk menjaga stabilitas suhu. Sistem dirancang menggunakan sensor suhu, modul termoelektrik (Peltier), dan antarmuka LCD untuk pengaturan set-point suhu secara digital. Hasil pengujian menunjukkan bahwa alat mampu mencapai suhu target 10°C dengan waktu respon rata-rata antara 17 hingga 22 menit dan error steady state sebesar 1%. Pengujian sensor suhu menunjukkan tingkat akurasi tinggi dengan rata-rata penyimpangan hanya 2,29%. Kesimpulannya, penggunaan efek Peltier yang dikendalikan oleh mikrokontroler efektif sebagai solusi pendingin portabel yang stabil dan ramah lingkungan.Kata kunci: Termoelektrik, Efek Peltier, Mikrokontroler ATmega328, PWM, Lemari Pendingin Pintar
Analisis Sentimen Publik Pada Media Sosial Tiktok Terhadap Program Makan Bergizi Gratis (MBG) Dengan Algoritma Support Vector Machine (SVM) Yudisti Prayigo Permana; Ikhwan Fauzi; Muhamad Ihsan Ashari
Algoritma: Jurnal Ilmu Komputer dan Informatika Vol 10, No 1 (2026): April 2026
Publisher : Universitas Islam Negeri Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/algoritma.v10i1.28894

Abstract

The rapid growth of social media has made digital platforms a primary space for expressing public opinion on government policies, including the Free Nutritious Meal Program (MBG). TikTok, as a widely used platform, allows users to share opinions openly through comments. This study aims to analyze public sentiment toward the MBG program based on TikTok comments using the Support Vector Machine (SVM) algorithm. Relevant comments were collected and classified into positive, neutral, and negative categories. The data then underwent preprocessing stages, including cleaning, case folding, normalization, tokenization, stopword removal, and stemming. Text data were transformed into numerical form using the TF-IDF method. The dataset was split into training and testing data with an 80:20 ratio. Results show that most comments are positive (60.64%), followed by neutral (32.94%) and negative (6.41%). The highest accuracy (79.71%) was achieved using linear and sigmoid kernels, indicating SVM’s effectiveness for sentiment analysis.
Analisis Kinerja Sistem Antrian Pelanggan Menggunakan FIFO dan Parallel Processing pada Warung Ayam Pecak Dhafa Hibrizi Sitorus; Dewi Aulia Tanjung; Ravil Baadilahasan Hrp; Chairil Umri
Algoritma: Jurnal Ilmu Komputer dan Informatika Vol 10, No 1 (2026): April 2026
Publisher : Universitas Islam Negeri Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/algoritma.v10i1.27786

Abstract

This study analyzes the customer queuing system at Warung Ayam Pecak by applying the First In First Out (FIFO) method combined with parallel processing to improve service efficiency. The selection of this topic is based on the problem of relatively long customer waiting times due to the sequential service process. Data collection was conducted through field observations to obtain patterns of customer arrivals, service duration, and cooking time under initial conditions using only one stove and one kitchen worker. Next, a simulation model was developed to compare the initial system with the proposed system, which implemented two parallel cooking processes. The simulation results showed that the application of parallel processing was able to reduce the Average Waiting Time (AWT) from 25 minutes to 2.82 minutes and the Average Waiting Process (AWP) from 11.82 minutes to 6.2 minutes. These findings indicate that the addition of simple resources, in the form of one stove and one worker, can significantly increase service throughput and reduce queue length. This study concludes that queue system optimization through the FIFO and parallel processing approaches is effective in improving the operational performance of small-scale culinary businesses.Keywords: Queue simulation, FIFO, Parallel Processing, Waiting Time, Operational Efficiency 
Klasterisasi Anggota Ikatan Arsitek Indonesia Sumatera Utara Menggunakan Algoritma K-Means Berbasis Framework Laravel Alif Saum Rizalitaher; Suendri Suendri; Imam Adlin Sinaga
Algoritma: Jurnal Ilmu Komputer dan Informatika Vol 10, No 1 (2026): April 2026
Publisher : Universitas Islam Negeri Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/algoritma.v10i1.26742

Abstract

The Indonesian Architects Association (IAI) of North Sumatra is a professional association that oversees architects in the North Sumatra region. In membership management, there are many inactive members, which risks causing a decline in membership numbers. This condition also has an impact on the development of the architectural profession in North Sumatra. This research aims to build a system that is able to cluster data on members who are at risk of leaving. The method used is the k-means algorithm, which is implemented in a web-based system. With this method, members are grouped into three main clusters based on their level of involvement. The research results show that the first cluster (C1) consists of 178 active members with high seniority (loyal). The second cluster (C2) includes new members who are active but still require further involvement (need engagement), with a total of 196 people. Meanwhile, the third cluster (C3) contains passive members who are at risk of leaving (retention target), with a total of 334 people. With this system, IAI North Sumatra can more easily identify and take strategic steps in maintaining and increasing the number of membership, so that it can support the development of the architectural profession in the area. Keywords: Clustering, K-means, Membership, IAI Sumut, Laravel
Pengembangan Sistem Layanan Praktikum Berbasis Web dengan Pendekatan Model Incremental Rio Rinaldi; Yusrizal Hakim
Algoritma: Jurnal Ilmu Komputer dan Informatika Vol 10, No 1 (2026): April 2026
Publisher : Universitas Islam Negeri Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/algoritma.v10i1.28724

Abstract

This study designs a web-based practicum management information system for the Computer Science Program at UIN North Sumatra. It addresses the inefficiencies of manual recording, which are prone to data loss and administrative delays. Developed using the Incremental model and PIECES analysis with PHP, MySQL, and Bootstrap, the system successfully optimizes administration and accelerates data recapitulation. However, mobile responsiveness remains limited. Future development suggests adding automated grading and user interface (UI) enhancements. Keywords: Information System, Incremental Model, PIECES Analysis, Digital Report
Perancangan Sistem Pemadam Api Otomatis Berbasis Internet of Things (IoT) Andres Nazwil Raksana Surbakti; Mahardika Abdi Prawira Tanjung
Algoritma: Jurnal Ilmu Komputer dan Informatika Vol 10, No 1 (2026): April 2026
Publisher : Universitas Islam Negeri Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/algoritma.v10i1.29416

Abstract

Fires are a common occurrence due to negligence in the use of electronic devices and can cause material losses and casualties. Conventional fire safety systems are considered ineffective because they rely on human response. This research aims to design and implement an Internet of Things (IoT)-based automatic fire extinguishing system that is capable of detecting and handling fires quickly and efficiently through a web-based application. The system uses an ESP32 microcontroller integrated with a fire sensor, an MQ-2 smoke sensor, a buzzer, a water pump, and a servo. The research method used is an experiment through the design, construction, and testing stages of the system. The results show that the system is able to detect the presence of fire from various directions, provide real-time notifications, and activate pumps and servos for the extinguishing process. Thus, the developed system can increase response speed and efficiency in handling fires. Keywords: Internet of Things (IoT), Fire Extinguishing System, ESP32, MQ-2 Smoke Sensor
Clustering of Crime-Prone Areas in East Medan Based on Police Data Using K-Means and DBSCAN Algorithms Gaizka Pasya Dermawan Sinukaban; Al-Khowarizmi Al-Khowarizmi
Algoritma: Jurnal Ilmu Komputer dan Informatika Vol 10, No 1 (2026): April 2026
Publisher : Universitas Islam Negeri Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/algoritma.v10i1.29498

Abstract

The Medan Timur sub-district is one of the high-crime areas in Medan City, recording 853 cases out of 1,308 criminal incidents collected by the Medan Timur Police Sector during the 2023–2025 period. The cases consist of motorcycle theft or curanmor (689 cases, 52.7%), aggravated theft or curat (448 cases, 34.3%), and robbery or curas (171 cases, 13.1%), spread across 20 sub-villages with a range of 13 to 162 cases per sub-village. This study clusters crime-prone areas using K-Means and DBSCAN algorithms and compares their performance through the Silhouette Index (SI) and Davies-Bouldin Index (DBI). The features used include total_kriminal, curanmor, curas, curat, and rata_waktu, normalized using Min-Max Normalization. The optimal number of clusters for K-Means was determined through the Elbow method yielding K=3, while DBSCAN parameters were determined through a KNN Distance Plot yielding eps=0.20 and minPts=2. Evaluation results show that K-Means yields SI=0.4105 (weak category) and DBI=1.2599, while DBSCAN yields SI=0.6788 (moderate category) and DBI=0.4986 on 8 non-noise sub-villages. DBSCAN outperforms K-Means on both metrics with an SI difference of 0.2683 and a DBI difference of 0.7613, although K-Means is superior in coverage by clustering all 20 sub-villages. These findings can be utilized by the Medan Timur Police Sector as a basis for determining priority patrol areas and allocating security resources more effectively. Keywords: Crime; Clustering; K-Means; DBSCAN; Silhouette Index; Davies-Bouldin Index 
Klasterisasi Masyarakat Kurang Mampu di Kelurahan Durian Kota Medan untuk Optimalisasi Penyaluran Bantuan Sosial Menggunakan Algoritma OPTICS Siti Nurisma Siregar; Yohanni Syahra
Algoritma: Jurnal Ilmu Komputer dan Informatika Vol 10, No 1 (2026): April 2026
Publisher : Universitas Islam Negeri Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/algoritma.v10i1.29496

Abstract

This study aims to classify underprivileged communities in Kelurahan Durian, Medan, to optimize social assistance distribution using the OPTICS algorithm. The socio-economic data used includes income, expenditure, occupation, education level, and number of dependents, comprising 800 records, which after preprocessing became 789 data points. The research stages include preprocessing, parameter determination through K-Distance Plot and Grid Search, the clustering process, and evaluation using the Silhouette Index. Optimal parameters were obtained at min_samples = 15, max_eps = 0.3, and xi = 0.030, yielding a Silhouette Index value of 0.2409. The clustering produced 4 clusters: unable, underprivileged, capable, and highly capable, along with a number of noise points. The OPTICS algorithm proved effective in identifying data structures with varying densities and automatically detecting outliers. Results were visualized through a reachability plot. This study is expected to improve the accuracy of targeted social assistance distribution through a data-driven approach. Keywords: Clustering, OPTICS, Data Mining, Social Assistance, Poverty
Sistem Pendukung Keputusan Prioritas Penerima MBG Berdasarkan Indeks Kemiskinan dengan MCDA-GIS Vivi Damayanti; Mahardika Abdi Prawira Tanjung
Algoritma: Jurnal Ilmu Komputer dan Informatika Vol 10, No 1 (2026): April 2026
Publisher : Universitas Islam Negeri Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/algoritma.v10i1.29527

Abstract

The accurate distribution of the Free Nutritious Meal Program (MBG) has become an important challenge in implementing data-driven social policies. Inaccurate prioritization of regions may lead to unequal aid distribution that does not reflect actual community needs. This study aims to develop a web-based Decision Support System by integrating Multi-Criteria Decision Analysis (MCDA) and Geographic Information System (GIS) methods to determine the priority of MBG beneficiaries in North Sumatra Province. The study employs poverty indicators published by the Central Statistics Agency (BPS), including the Poverty Rate (PO), Poverty Gap Index (P1), and Poverty Severity Index (P2). Criteria weighting was conducted using the Analytic Hierarchy Process (AHP), while regional ranking was performed using the Weighted Linear Combination (WLC) method. The results indicate that the system successfully classified 33 regencies/cities into high, medium, and low priority categories, which were visualized through GIS-based thematic maps. Validation was conducted through AHP consistency testing with a Consistency Ratio (CR) value of < 0.1 and functional system testing showing that all features operated properly. The novelty of this study lies in the integration of poverty-based MCDA-GIS to support more systematic, informative, and integrated spatial visualization of MBG beneficiary priorities. Keywords: Decision Support System, MCDA, AHP, WLC, GIS.
Komparasi Algoritma KNN Dan SVM Untuk Klasifikasi Kesehatan Mental Pada Usia Remaja Rizky Hidayat Hasibuan; Mulkan Azhari
Algoritma: Jurnal Ilmu Komputer dan Informatika Vol 10, No 1 (2026): April 2026
Publisher : Universitas Islam Negeri Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/algoritma.v10i1.28151

Abstract

Mental health among adolescents is a critical issue that continues to increase and is often not detected early due to the limitations of assessment methods that remain largely subjective. This study aims to classify adolescent mental health levels based on stress levels using a machine learning approach and to compare the performance of the KNN and SVM algorithms. The dataset used consists of 1,100 adolescent records obtained from GitHub, comprising 11 predictor attributes and one target attribute, namely stress_level, which is classified into three categories: low, moderate, and high. The research stages include data preprocessing, EDA, feature selection, handling class imbalance using the SMOTE, modeling, and evaluation. Model testing was conducted using several training–testing split ratios. Model performance was evaluated using confusion matrix. The results indicate that the SVM algorithm achieved the best performance with an accuracy of 89.55% and an F1-Score of 89.58% using an 80:20 data split prior to the application of SMOTE. Overall, SVM demonstrated higher stability and accuracy compared to KNN in classifying adolescent mental health levels, indicating its strong potential as a data-driven early detection tool for adolescent mental health issues. Keywords: Adolescent Mental Health, Stress Level Classification, K-Nearest Neighbor, Support Vector Machine

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