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All Journal Jurnal Ilmu Komputer Jurnal Transformatika Jurnal Edukasi dan Penelitian Informatika (JEPIN) INFORMAL: Informatics Journal Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JOURNAL OF APPLIED INFORMATICS AND COMPUTING METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi ILKOM Jurnal Ilmiah Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) JOISIE (Journal Of Information Systems And Informatics Engineering) INFOKUM Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Computer Science and Information Technology (CoSciTech) International Journal of Engineering, Science and Information Technology Jurnal Informatika dan Teknologi Komputer ( J-ICOM) Multica Science and Technology jeti Jurnal Minfo Polgan (JMP) Jurnal Pengabdian Masyarakat : Pemberdayaan, Inovasi dan Perubahan TECHSI - Jurnal Teknik Informatika Sisfo: Jurnal Ilmiah Sistem Informasi Journal of Information Technology (JINTECH) International Journal of Information System & Innovative Technology Jurnal Pengabdian Masyarakat Bangsa Jurnal Malikussaleh Mengabdi Journal of Advanced Computer Knowledge and Algorithms Gameology and Multimedia Expert International Journal of Information System and Innovative Technology Smatika Jurnal : STIKI Informatika Jurnal Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Proceedings of Malikussaleh International Conference on Multidisciplinary Studies (MICoMS)
Sujacka Retno
Universitas Malikussaleh
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Identification of Environmental Security in Relation to Crime Rates in Simeulue Regency Using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) Method Yopy Anfelia; Munirul Ula; Sujacka Retno
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Criminal offenses are acts that violate criminal law and are punishable by the state, either through imprisonment, fines, or other sanctions. These offenses cause significant distress and harm to the general public, individuals, and the state. In Simeulue Regency, the number of criminal cases has been increasing annually, driven by social, economic, environmental, cultural, legal, technological, and psychological factors. This study aims to analyze the relationship between environmental security and the level of criminal cases in Simeulue Regency using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. The data used includes criminal cases from 2019 to 2023 across 10 districts, along with environmental information such as population density, public facilities, and socioeconomic indicators. The research methodology involves data collection and cleaning, Euclidean distance calculation, parameter selection for DBSCAN, and the application of validation formulas to determine the vulnerability to criminal offenses in Simeulue Regency. The analysis results, using an epsilon parameter of 5 and MinPts of 3, yielded clusters 0, -1, and 1. Cluster 0 includes Salang and Teluk Dalam districts; cluster -1 includes Alafan, Simeulue Tengah, Simeulue Timur, Simeulue Barat, Teupah Barat, and Teupah Selatan districts; and cluster 1 includes Simeulue Cut and Teupah Tengah districts. The validation formula indicates that the highly vulnerable area is in Simeulue Timur district, while the at-risk areas are Teupah Tengah, Teluk Dalam, and Teupah Barat districts. The areas classified as not at risk are Alafan, Salang, Simeulue Tengah, Simeulue Cut, Simeulue Barat, and Teupah Selatan districts. This study provides insights into areas that require increased attention in efforts to address and prevent criminal offenses. Keywords: environmental security, criminal offenses, DBSCAN, clustering, Simeulue Regency
ANALISIS CLUSTERING DAERAH PRODUKTIVITAS PADI DI KABUPATEN DELI SERDANG MENGGUNAKAN ALGORITMA ISOLATION FOREST Ardi Wirya Indarto; Asrianda; Sujacka Retno
Journal of Information Technology Vol. 7 No. 1 (2026): Februari 2026
Publisher : Prodi Teknologi Informasi UIN Ar-Raniry Bekerjasama dengan Pusat Penelitian dan Penerbitan LP2M Universitas Islam Negeri Ar-Raniry Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22373/jintech.v7i1.9617

Abstract

Rice is a major food commodity that plays a vital role in national food security. However, differences in rice productivity levels between regions pose a challenge in formulating targeted agricultural policies. This study aims to analyze and cluster rice productivity areas in Deli Serdang Regency using the Isolation Forest algorithm. The data used are rice productivity data from all sub-districts in Deli Serdang Regency for the period 2020–2024, with variables of planted area, harvested area, production volume, and rice productivity. The analysis process is carried out through a web-based system using the Python programming language with the Streamlit framework. The Isolation Forest algorithm is used for clustering and anomaly detection, while cluster quality is evaluated using the Silhouette Score. The results of the 2024 data analysis show that 22 sub-districts in Deli Serdang Regency are divided into four clusters: a high-productivity cluster of 7 sub-districts (31.82%) with an average productivity above 6.2 tons/ha, a medium-productivity cluster of 4 sub-districts (18.18%) with a productivity of 6.0–6.1 tons/ha, a low-productivity cluster of 7 sub-districts (31.82%) with a productivity of around 5.9–6.0 tons/ha, and an anomalous cluster of 4 sub-districts (18.18%). The results of this clustering are expected to assist local governments in determining policies to increase rice productivity more effectively and based on data.
Enhancing Academic Security with RFID-Based Smart Locks and Real-Time Attendance Tracking System Muhammad Al Imran; Muhammad Fikry; Sujacka Retno
Proceedings of Malikussaleh International Conference on Multidisciplinary Studies (MICoMS) Vol. 4 (2024): Proceedings of Malikussaleh International Conference on Multidisciplinary Studies (MI
Publisher : LPPM Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/micoms.v4i.950

Abstract

In this study, we propose a novel RFID-based smart lock system integrated with real-time attendance tracking to enhance academic security. Traditional security methods such as mechanical locks and manual attendance systems are prone to various limitations, including lost keys, falsification, and lack of automatic tracking. Our system utilizes E-KTP cards as RFID identification tools, supported by Internet of Things (IoT) technology, to provide automated door access and efficient attendance monitoring. The implementation results demonstrate a high accuracy rate of 99.5% in reading E-KTP cards, with an average response time of 850 Ms and a 99.5% uptime during a 30-day testing period. The system can handle up to 40 access requests per minute during peak hours. Additionally, it reduces access time by 91%, lowers errors from 5% to 0.2%, cuts operational costs by 60%, and decreases maintenance time by 75%. Security is reinforced through dual encryption using the Vigenère and Bcrypt algorithms, ensuring no security breaches over six months. The dashboard provides real-time monitoring, and the automated attendance system reduces human error, integrating seamlessly with academic databases for user verification and schedule management. This research demonstrates the effectiveness of RFID and IoT technologies in modernizing and securing academic environments.
Indonesian Sign Language (BISINDO) Alphabet Detection System Using YOLO (You Only Look Once) Algorithm Andra Munandar; Zara Yunizar; Sujacka Retno
Proceedings of Malikussaleh International Conference on Multidisciplinary Studies (MICoMS) Vol. 4 (2024): Proceedings of Malikussaleh International Conference on Multidisciplinary Studies (MI
Publisher : LPPM Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/micoms.v4i.952

Abstract

This research aims to develop an Indonesian Sign Language (BISINDO) alphabet detection system using the YOLOv5 algorithm, an efficient and fast deep learning-based object detection model. The dataset used consists of BISINDO alphabet images enriched through data augmentation techniques such as rotation, flipping, and brightness adjustment. The evaluation results show that the YOLOv5s model achieved very good performance, with an average precision of 85.2%, recall of 89.3%, F1-score of 87.2%, and mean average precision (mAP) of 87.1%. The confusion matrix also indicates the model's ability to differentiate each BISINDO alphabet with high accuracy. The training data testing showed the model successfully achieved consistent decreases in all loss components, such as a decrease in train box loss from 0.06 to 0.015, and validation loss converging towards 0.002 for object loss and class loss. The real time testing also shows that the YOLOv5-based BISINDO alphabet detection system can perform well and consistently, indicating the practical application potential of this system to facilitate communication between people with hearing/speech disabilities and the general public. Overall, this research has resulted in an accurate and realtime implementable BISINDO alphabet recognition system.
A Hierarchical Weighted Role Attribute Decision Support Framework for Automated Best XI Selection and Formation Strategy in Football Manager Simulation Retno, Sujacka
Gameology and Multimedia Expert Vol. 3 No. 2 (2026): Gameology and Multimedia Expert - April 2026 (In Press)
Publisher : Department of Informatics Faculty of Engineering Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/game.v3i2.26743

Abstract

Football team management involves complex decision-making processes that require evaluating high-dimensional player attributes while accounting for positional roles and tactical structures. Modern football simulation environments, such as Football Manager, provide rich datasets suitable for exploring decision-support approaches to lineup selection and formation planning. This paper proposes a hierarchical role-based scoring framework for automated best XI selection and formation-based role assignment. The method organizes player attributes into role-specific groups and applies weighted aggregation to reflect positional relevance, producing interpretable role scores for eight football positions. The framework is evaluated using a full-season simulation on a real club dataset, comparing three lineup strategies: default AI tactics, mean-based attribute scoring, and the proposed hierarchical weighting approach. Results demonstrate that the weighted scoring framework achieves substantially improved league performance under the evaluated conditions, as reflected by higher points accumulation and improved goal difference compared to baseline methods. The findings highlight the importance of structured role modeling in football analytics and support the use of simulation-based environments as valid testbeds for decision-support systems. The proposed approach is intended to assist, rather than replace human managers, offering analytical recommendations that enhance tactical decision-making in football simulations and serious games.
COMPARING SIMPLE EXPONENTIAL SMOOTHING AND ADVANCED TIME SERIES FORECASTING FOR CEMENT STOCK PREDICTION AT PT. SOLUSI BANGUN ANDALAS Uzia Ulfa; Sujacka Retno; Safwandi
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 10 No 2 (2025): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v10i2.6483

Abstract

Accurate cement stock prediction is crucial for optimizing supply chain management, ensuring operational efficiency, and achieving long-term sustainability and profitability within the global cement industry. Inaccurate predictions can lead to significant costs due to overstocking or stockouts, impacting customer satisfaction and overall economic development. The complex market environment and dynamic demand fluctuations within the construction sector further exacerbate these challenges. This study compares time series forecasting algorithms to predict cement stock levels. The methodologies investigated include traditional statistical models: Simple Moving Average (SMA), Double Moving Average (DMA), Simple Exponential Smoothing (SES), and Double Exponential Smoothing (Holt's Method). Additionally, advanced machine learning and deep learning models, namely ARIMA (Autoregressive Integrated Moving Average), LSTM (Long Short-Term Memory), and Prophet, are also evaluated. This research aims to identify the most suitable algorithm for cement stock forecasting by assessing their performance using standard metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). Initial findings from existing literature suggest that while traditional methods offer simplicity, modern models like LSTM often achieve superior accuracy for complex and non-linear patterns, whereas Prophet excels at automatically handling seasonality and missing data.ARIMA provides computational efficiency for simpler, stationary patterns but may struggle with non-linearity. This study contributes to the field by providing a structured comparison of diverse forecasting techniques specifically tailored for cement inventory, offering practical guidance for industry practitioners and informing strategic decision-making in supply chain optimization.  
Clustering the Spread of ISPA Disease Using the Fuzzy C-Means Algorithm in Aceh Utara Rozzi Kesuma Dinata; Bustami Bustami; Sujacka Retno; Azrai Putra Barumun Daulay
International Journal of Information System and Innovative Technology Vol. 1 No. 2 (2022): December
Publisher : Geviva Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63322/901yj796

Abstract

ISPA disease is one of the most common types of disease suffered in Aceh. The method used in this research is fuzzy c means algorithm to cluster the spread of ISPA disease areas in Aceh Utara. The data used is the data on patients with ISPA in 2021 obtained from Cut Meutia General Hospital and Arun Hospital. The attributes used consist of the patient's age, patient's address, patient's gender and the type of ISPA the patient suffered. In this research only focused on 3 types of ISPA, namely Pneumonia, Bronchitis and ISPA. The existing data will then be grouped into 3 clusters, namely low cluster, medium cluster and high cluster. The results of clustering the spread of ISPA in Aceh Utara show that the highest case is in Dewantara District, the medium cluster are Muara Batu, Nisam, Nisam Antara, Sawang, and Tanah Jambo Aye Districts, and the low cluster is Baktiya, Baktiya Barat, Banda Baro, Cot Girek, Geureudong Pase, Kuta Makmur, Langkahan, Lapang, Lhoksukon, Matang Kuli, Meurah Mulia, Nibong, Paya Bakong, Pirak Timu, Samudera, Seunudon, Simpang Keramat, Syamtalira Aron, Syamtalira Bayu, Tanah Luas and Tanah Pasir Districts. With the results of this clustering, it is expected that the provision of drug stocks for ISPA will be prioritized in cluster areas with the highest spread rates.
Sistem Informasi Pelayanan Cuti Berbasis Web Pada PT Pupuk Iskandar Muda Menggunakan PHP dan MySQL Sujacka Retno; Lidya Rosnita; Said Fadlan Anshari
TECHSI - Jurnal Teknik Informatika Vol. 14 No. 1 (2023)
Publisher : Teknik Informatika Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/techsi.v14i1.12076

Abstract

Pada era-globalisasi ini teknologi informasi menjadi alat dasar yang dibutuhkan oleh setiap perusahaan yang ada. Dengan menggunakan teknologi informasi keakuratan dan kecepatan akses data akan lebih mudah di jalankan. PT Pupuk Iskandar Muda merupakan pabrik pupuk urea ke-11 di Indonesia dan pabrik ke-2 di Provinsi Aceh. Proses pengelolaan cuti pada PT Pupuk Iskandar Muda saat ini masih dilakukan secara manual. Proses pengelolaan cuti tersebut memiliki beberapa kelemahan. Karyawan tidak bisa mengetahui sisa hak cuti pribadi dan pengambilan cuti oleh rekan kerja secara langsung, sehingga karyawan tidak bisa melakukan manajemen cuti dengan baik.Pimpinan juga belum dapat mengambil keputusan cuti berdasarkan prinsip pemerataan hak cuti karyawan. Kelemahan yang lain adalah proses pengurusan cuti karyawan kurang efektif dan efesien. Dalam menyelesaikan masalah tersebut, penulis merancang sebuah sistem dengan menggunakan pemodelan ERD dan DFD, Personal Home Page (PHP) dan menggunakan basis data MySQL. Dengan adanya sistem pelayanan cuti di PT Pupuk Iskandar Muda ini, karyawan akan bisa lebih mudah untuk mengakses masalah percutian.
Penerapan Sistem Deteksi Pengisian Ruang Parkir Kendaraan Roda 4 Menggunakan Metode Computer Vision Di Orbit Future Academy Lidya Rosnita; Sujacka Retno
TECHSI - Jurnal Teknik Informatika Vol. 15 No. 1 (2024)
Publisher : Teknik Informatika Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/techsi.v15i1.16142

Abstract

Keberadaan kecerdasan buatan (AI) telah mengubah lanskap teknologi dan membawa perubahan signifikan bagi kehidupan manusia. Di Indonesia terdapat perusahaan Orbit Future Academy (OFA) yang berfokus pada program Artificial Intelligence 4Jobs (AI 4JOBS). AI 4JOBS bertujuan untuk memperkuat kompetensi individudalam kecerdasan buatan (AI) sebagai persiapan untuk terjun ke dunia kerja yangterus berkembang. Program AI 4JOBS di OFA dirancang dengan beragam modulyang mencakup pemahamankonsep AI, keterampilan teknis, aspek etika profesi,dan kesiapan berkarir. Dalam penelitian ini berfokus pada" Penerapan Sistem Deteksi Pengisian Ruang Parkir Kendaraan Roda Menggunakan Metode Computer Vision".Untuk menyelesaikan tugas tersebut, sebuah website AI dibangun dengan memanfaatkan domain AI Computer Vision  dengan ruang warna HSV (Hue, Saturation, Value) dan library OpenCV adalah pendekatan yang umum digunakan dalam pengolahan citrauntuk membedakan kendaraan dari latar belakanguntukmengidentifikasitata dalam pengaturan parkir kendaraan roda 4.Melaluiprogram AI 4JOBS di OFA, peneliti berhasil memperoleh pengetahuan yang luastentangAIdanmengasahketerampilanteknisyangsangatdibutuhkandalammenghadapiperkembanganteknologiAI. Selainitu,programinijugamemberiwawasantentangetikaprofesidankesiapanberkarirdieraAI.
EVALUASI KINERJA K-MEDOIDS CLUSTERING MODEL UNTUK KLASTERISASI DAERAH PRODUKTIVITAS PANEN PADI DI KABUPATEN BIREUEN Hayatun Nisa; Muhammad Daud; Sujacka Retno
JOISIE (Journal Of Information Systems And Informatics Engineering) Vol. 9 No. 2 (2025)
Publisher : Institut Bisnis dan Teknologi Pelita Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35145/joisie.v9i2.4965

Abstract

Produktivitas padi merupakan indikator penting dalam memantau dan meningkatkan produksi padi di suatu wilayah. Di Kabupaten Bireuen, Aceh, alokasi pupuk subsidi yang tidak merata menjadi kendala dalam optimalisasi produktivitas padi. Penelitian ini bertujuan untuk mengklaster daerah prioritas dan non-prioritas produktivitas panen padi di Kabupaten Bireuen menggunakan algoritma K-Medoids dan Purity K-Medoids. Data yang digunakan adalah data historis pertanian padi tahun 2012–2023 sebanyak 204 record, yang diperoleh dari Badan Pusat Statistik Kabupaten Bireuen dan Dinas Pertanian dan Perkebunan Kabupaten Bireuen, dengan variabel seperti jumlah desa, luas tanam, luas panen, produktivitas, jumlah produksi, dan persentase luas tanam. Proses klasterisasi dievaluasi menggunakan Davies Bouldin Index (DBI). Hasil penelitian menunjukkan bahwa Purity K-Medoids menghasilkan nilai rata-rata DBI sebesar 0,786911, lebih rendah dibandingkan K-Medoids yang sebesar 0,907856, menandakan validitas klaster yang lebih baik. Berdasarkan hasil klasterisasi, Kecamatan Peusangan paling sering muncul sebagai daerah prioritas, yaitu pada tahun 2013, 2015, 2016, 2018, 2019, 2020, 2022, dan 2023. Temuan ini diharapkan dapat menjadi pertimbangan dalam kebijakan alokasi sumber daya pertanian di Kabupaten Bireuen.
Co-Authors Abdul Azis Andra Munandar Angga Pratama Ardi Wirya Indarto Asmaul Husna Asrianda Asrianda Aulia, Faizul Azrai Putra Barumun Daulay Bustami Bustami Bustami Cut Agusniar Devi, Salma EDI YUSUF, EDI Fadlisyah Fadlisyah Fahrizal, Effan Fajri, T Irfan Fiasari, Fiasari Fikran, Rifzan Fortilla, Zeny Arsya Gadis Ayu Sofiana Gilang Wahyu Ramadhan Gilang Hakimi, Musawer Haried Novriando Hayatun Nisa Hidayatsyah Hidayatsyah ilham - sahputra Ilham Sahputra Ilmi Suciani Sinambela Ima Pratiwi Irvan Na’syakban Lidya Rosnita Lubis, Syahrul Andika Maghfirah, Riezka Mahsa, Masithah Maida, Eka Maryana Maryana Maryana Maryana Maryana, Maryana Muhammad Al Imran Muhammad Daud Muhammad Fikry Muhammad Ikhwanus Muhammad Nurfahmi Muhammad, Muhammad Munirul Ula Mutammimul Ula Mutasar Nadia Saphira Nasrul ZA, Nasrul Nisa Ul Fadila Novia Hasdyna Nur Faliza Nurdin Nurdin Panjaitan, Cherlina Helena Purnamasari Rahma Fitria, Rahma Reza Pahlevi Ginting Richki Hardi Rijal, Himmatur Rini Meiyanti Rizky Putra Fhonna Rizkya, Dini Dara Rozzi Kesuma Dinata Safriandi, Safriandi Safwandi Safwandi Safwandi, Safwandi Sahputra, Ilham Said Fadlan Anshari Salsabila, Thifal Sayed Fachrurrazi Selly Alfika Sinambela, Ilmi Suciani Siti Fatimatun Zahro Siti Wahyuni Sudirman Sudirman T Irfan Fajri Taskia, Narita Teuku Zulkarnaen Tsania Asha Fadilah Daulay Utari, Sylva Putri Uzia Ulfa Veri Ilhadi Wahdana, Aldi Wahyu Isnanda Nasution Wibowo, Patmono Yafis, Balqis Yanti, Riski Yesy Afrillia Yopy Anfelia Zara Yunizar Zulfadl, Zulfadl Zulfia , Anni