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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) ITEJ (Information Technology Engineering Journals)
Sujacka Retno
Universitas Malikussaleh
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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.
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.
SISTEM E-ARSIP SURAT BERBASIS WEB PADA DINAS KOMUNIKASI INFORMATIKA DAN PERSANDIAN KAB. ACEH TAMIANG Sujacka Retno; Rozzi Kesuma Dinata; Selly Alfika
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 6 No. 2 (2022): Sisfo: Jurnal Ilmiah Sistem Informasi, Oktober 2022
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v6i2.10296

Abstract

Arsip adalah catatan rekaman kegiatan atau sumber informasi dengan berbagai macam bentuk yang dibuat oleh lembaga, organisasi maupun perseorangan dalam rangka pelaksanaan kegiatan. Tidak terkecuali pada sistem arsip kedinasan untuk menandai surat masuk dan surat keluar yang masih bersifat manual, dimana hal ini sangat tidak efesien karena surat masuk maupun surat keluar yang ada bisa saja terselip, hilang dan robek. Oleh karena itu sangat perlu untuk merubah sistem arsip manual menjadi sistem arsip berbasis web supaya membantu proses penyimpanan data surat. Sehingga dapat meningkatkan kualitas sistem pada layanan arsip surat. Dalam penelitian ini akan dirancang sebuah sistem arsip surat berbasis web.
Sistem Pendataan Inventaris Barang Pada Program Studi Teknik Informatika Universitas Malikussaleh Sujacka Retno; Cut Agusniar; Ilmi Suciani Sinambela
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 7 No. 2 (2023): Sisfo: Jurnal Ilmiah Sistem Informasi, Oktober 2023
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v7i2.13944

Abstract

Sistem pendataan inventaris barang adalah sebuah alat bantu untuk mencatat dan mendata seluruh barang-barang dengan cara yang terstruktur. Sistem ini dibangun untuk mengatasi permasalahan yang ada di Program Studi Teknik Informatika yang mana dalam mendata inventaris masih bersifat manual, dimana hal ini sangat tidak efesien karena berkas yang disusun cenderung lama dan tidak efektif untuk dikelola secara berkala. Oleh karena itu sangat perlu untuk merubah sistem pendataan inventaris yang manual menjadi sistem berbasis komputer supaya membantu proses penyimpanan data menjadi lebih efektif. Sehingga dapat meningkatkan kualitas sistem pada layanan pengelolaan data inventaris. Hasil penelitian ini dapat mendata inventaris barang di prodi Teknik Informatika. Sistem informasi yang akan dibuat ini menggunakan database secara terpusat dan berbasis desktop.
Evaluating The Quality of K-Medoids Clustering on Crime Data in Indonesia Sujacka Retno; Rozzi Kesuma Dinata; Novia Hasdyna
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 8 No. 2 (2024): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol8No2.pp274-280

Abstract

This study evaluates the quality of K-Medoids clustering applied to criminal incident data in Indonesia from 2000 to 2023. The analysis compares the clustering performance on both original and normalized datasets using various evaluation metrics, including the Davies-Bouldin Index (DBI), Silhouette Score (SS), Normalized Mutual Information (NMI), Adjusted Rand Index (ARI), and Calinski-Harabasz Index (CH). The findings reveal that the original dataset consistently outperforms the normalized dataset across all metrics. The optimal clustering was achieved in the seventh iteration of the original data, with the lowest DBI (0.438), the highest SS (0.683), NMI (0.916), ARI (0.984), and CHI (57.418). In contrast, the normalized data exhibited higher DBI values and, in some cases, negative Silhouette Scores, indicating less distinct clusters. These results suggest that for this dataset, K-Medoids clustering performs more effectively on the original data without normalization, providing more accurate and well-defined clusters of criminal incidents. This insight is crucial for future research and practical applications in crime data analysis, emphasizing the importance of dataset preprocessing in clustering methodologies.
A Hierarchical Weighted Role Attribute Decision Support Framework for Automated Best XI Selection and Formation Strategy in Football Manager Simulation Sujacka Retno
Gameology and Multimedia Expert Vol. 3 No. 2 (2026): Gameology and Multimedia Expert - April 2026
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.
Co-Authors Abdul Azis Andra Munandar Angga Pratama Ardi Wirya Indarto Asrianda Asrianda Asrillah Asrillah Aulia, Faizul Azrai Putra Barumun Daulay Beno Jange Bustami Bustami Bustami Cut Agusniar Devi, Salma EDI YUSUF, EDI Ekamaida, Ekamaida 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 Maghfirah, Riezka Mahsa, Masithah Mansur Mansur 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 Narita Taskia Nasrul ZA, Nasrul Nisa Ul Fadila Novia Hasdyna Nur Faliza Nurdin Nurdin Panjaitan, Cherlina Helena Purnamasari Pathia Pathia 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 Sayed Fachrurrazi Selly Alfika Sinambela, Ilmi Suciani Siti Fatimatun Zahro Siti Wahyuni Sudirman Sudirman Syahrul Andika Lubis T Irfan Fajri Taufiq Taufiq Teuku Zulkarnaen Thifal Salsabila 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