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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Techno.Com: Jurnal Teknologi Informasi Jurnal Edukasi dan Penelitian Informatika (JEPIN) JUSIFO : Jurnal Sistem Informasi Jurnal Pendidikan Informatika dan Sains Jurnal Khatulistiwa Informatika JMM (Jurnal Masyarakat Mandiri) CYBERNETICS METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi BULETIN AL-RIBAATH VOX EDUKASI: Jurnal Ilmiah Ilmu Pendidikan Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Progresif: Jurnal Ilmiah Komputer JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Teknika Indonesian Journal of Data and Science Jurnal Computer Science and Information Technology (CoSciTech) JUTECH : Journal Education and Technology Joutica : Journal of Informatic Unisla International Journal of Health, Engineering and Technology International Journal of Health and Information System (IJHIS) SmartComp Jurnal Informatika Polinema (JIP) Jurnal Medika: Medika Jurnal Ragam Pengabdian LANCAH Jurnal Inovasi dan Tren
Alda Cendekia Siregar
Universitas Muhammadiyah Pontianak
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Perbandingan Model Arsitektur CNN Dengan Metode Transfer Learning Untuk Klasifikasi Spesies Ikan Laut Anugrah, Ilham; Siregar, Alda Cendekia; Octariadi, Barry Ceasar
Progresif: Jurnal Ilmiah Komputer Vol 20, No 1: Februari 2024
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v20i1.1834

Abstract

Fish are cold-blooded animals that are widely utilized by humans. Fish are a diverse group of poikilothermic vertebrates with over 27,000 species worldwide. The large number of fish species poses a challenge in distinguishing between them. This study aims to implement and compare three popular Convolutional Neural Network (CNN) architectures, namely Xception, Nasnet, and MobileNet V3L, in the task of classifying marine fish species. The research results show that all three CNN architectures perform exceptionally well in classifying marine fish species. The MobileNet V3L architecture achieves the highest level of accuracy with a value of 99% for all evaluation metrics. The Xception architecture achieves an overall accuracy of 97%, while the NasNet architecture achieves an overall accuracy of 92%. Overall, the comparison indicates that the MobileNet V3L architecture is the best architecture in this study.Keyword: Convolutional Neural Network; Transfer Learning; Sea Fish Classification AbstrakIkan merupan hewan berdarah dingin yang banyak di manfaatkan oleh manusia. Ikan adalah kelompok vertebrata poikilotermik yang beraneka ragam dengan jumlah spesies lebih dari 27.000 di seluruh dunia. Banyaknya jumlah spesies ikan menjadi satu masalah dalam membedekan jenis ikan. Penelitian inibertujuan untuk menerapkan dan membandingkan tiga arsitektur Convolutional Neural Network (CNN) yang populer, yaitu Xception, Nasnet, dan MobileNet V3L, dalam tugas klasifikasi spesies ikan laut. Hasil penelitian menunjukkan bahwa semua tiga arsitektur CNN memiliki performa yang sangat baik dalam klasifikasi spesies ikan laut. Arsitektur MobileNet V3L mencapai tingkat akurasi tertinggi dengan nilai sebesar 99% untuk semua metrik evaluasi. untuk arsitektur Xception menghasilkan akurasi keseluruhan 97% dan arsitektur NasNet menghasilkan akurasi keseluruhan 92%. Secara keseluruhan perbandingan dapat dikatakan bahwa arsitektur Mobilenet V3L adalah arsitektur terbaik dalam penelitian ini.Kata kunci: Convolutional Neural Network; Transfer Learning; Klasifikasi Ikan Laut
Klasifikasi Penyakit Daun Tanaman Timun Berbasis Convolutional Neural Network (CNN) Yanto, Maryogi; Siregar, Alda Cendekia; Abdullah, Asrul
Computer Science and Information Technology Vol 6 No 2 (2025): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v6i2.9982

Abstract

Penyakit daun pada tanaman mentimun merupakan salah satu tantangan utama dalam meningkatkan hasil panen, terutama di Kalimantan Barat. Identifikasi penyakit secara manual seringkali tidak akurat dan memakan waktu. Penelitian ini bertujuan untuk mengembangkan sistem klasifikasi otomatis untuk penyakit daun mentimun berbasis Convolutional Neural Network (CNN) menggunakan arsitektur VGG-16. Dataset terdiri dari 2.000 citra daun mentimun yang dikategorikan ke dalam lima kelas: Bercak Daun Bakteri, Penyakit Bulai Berbulu, Daun Sehat, Penyakit Mosaik, dan Penyakit Bulai Tepung. Metode yang diterapkan meliputi praproses (pengubahan ukuran, augmentasi, normalisasi), pelatihan model, pengujian, dan evaluasi menggunakan metrik akurasi, presisi, recall, dan skor F1. Model mencapai akurasi 88% pada data pelatihan, 84% pada data validasi, dan 81,50% pada data pengujian. Model yang telah dilatih kemudian diintegrasikan ke dalam aplikasi berbasis web menggunakan Streamlit untuk memfasilitasi klasifikasi interaktif. Hasilnya menunjukkan bahwa Jaringan Saraf Konvolusional (CNN) efektif dalam mengklasifikasikan penyakit daun mentimun secara otomatis dan dapat diterapkan sebagai solusi teknologi di bidang pertanian.
Prediction of Diabetes Mellitus Using the Case-Based Reasoning Method Rahimah, Auliyya; Siregar, Alda Cendekia; Pangestika, Menur Wahyu
Computer Science and Information Technology Vol 6 No 3 (2025): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v6i3.10266

Abstract

Diabetes Mellitus (DM) is a chronic disease that can lead to serious complications if not detected and treated early. According to data from WHO and the Indonesian Ministry of Health, the prevalence of DM continues to rise each year, highlighting the need for a diagnostic support system that is both fast and accurate. This study aims to develop an expert system capable of predicting Diabetes Mellitus using the Case Based Reasoning (CBR) method. CBR is applied because it solves new problems by comparing them to previous cases based on the similarity of symptoms. The system incorporates 20 symptoms classified into two types of DM: type 1 and type 2. The prediction process follows the four main stages of CBR: retrieve, reuse, revise, and retain. Test results show that the system can predict the disease with an accuracy rate of over 90%, and user feedback through Blackbox Testing and User Acceptance Testing (UAT) confirms its usability. This expert system is expected to serve as an initial consultation tool to help users obtain early information related to potential DM quickly, easily, and efficiently.
Implementasi Ant Colony Optimization Untuk Rute Terpendek Pada Pengiriman Barang J&T Cahyo, Rahandya; Siregar, Alda Cendekia; Octariadi, Barry Ceasar
Computer Science and Information Technology Vol 6 No 3 (2025): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v6i3.10467

Abstract

Parcel delivery is a logistics service that requires speed and efficiency, especially in determining delivery routes. The choice of this topic is based on the problem faced by J&T delivery in Kubu Raya, particularly Desa Kapur, where long travel distances often result in inefficiency. This study applies the Ant Colony Optimization (ACO) algorithm to identify the shortest route for parcel delivery. ACO mimics the behavior of ants in finding optimal paths based on pheromone intensity. Location data were obtained using coordinates from the Google Maps API and modeled into a weighted graph, where nodes represent delivery points and edges represent distances. The optimization process was carried out by simulating the movement of ant agents to evaluate alternative routes, followed by pheromone updates on the more efficient paths. The results indicate that ACO successfully generated more efficient delivery routes compared to conventional methods, achieving a distance reduction of 28.29%, equivalent to approximately 10.68 km saved. This efficiency contributes to reduced travel time and operational costs. The optimized routes were also visualized through an interactive map using Leaflet.js to facilitate analysis and interpretation. Therefore, ACO is proven to be effective in optimizing delivery routes and has strong potential for real-world application in courier services.
Implementasi Ant Colony Optimization Untuk Rute Terpendek Pada Pengiriman Barang J&T Cahyo, Rahandya; Siregar, Alda Cendekia; Octariadi, Barry Ceasar
Computer Science and Information Technology Vol 6 No 3 (2025): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v6i3.10467

Abstract

Parcel delivery is a logistics service that requires speed and efficiency, especially in determining delivery routes. The choice of this topic is based on the problem faced by J&T delivery in Kubu Raya, particularly Desa Kapur, where long travel distances often result in inefficiency. This study applies the Ant Colony Optimization (ACO) algorithm to identify the shortest route for parcel delivery. ACO mimics the behavior of ants in finding optimal paths based on pheromone intensity. Location data were obtained using coordinates from the Google Maps API and modeled into a weighted graph, where nodes represent delivery points and edges represent distances. The optimization process was carried out by simulating the movement of ant agents to evaluate alternative routes, followed by pheromone updates on the more efficient paths. The results indicate that ACO successfully generated more efficient delivery routes compared to conventional methods, achieving a distance reduction of 28.29%, equivalent to approximately 10.68 km saved. This efficiency contributes to reduced travel time and operational costs. The optimized routes were also visualized through an interactive map using Leaflet.js to facilitate analysis and interpretation. Therefore, ACO is proven to be effective in optimizing delivery routes and has strong potential for real-world application in courier services.
Identifikasi Penyakit Daun Cabai Menggunakan Arsitektur DenseNet169 Setiarini, Putri Rizka; Oktariadi, Barry Ceasar; Siregar, Alda Cendekia
Computer Science and Information Technology Vol 6 No 3 (2025): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v6i3.10631

Abstract

Chili is a high-value agricultural commodity in Indonesia, but its production is often hindered by leaf diseases such as spots, curling, and yellowing. Early identification of these diseases is crucial to prevent significant yield losses. This study aims to develop an automated system for identifying chili leaf diseases using the DenseNet169 Deep Learning architecture, implemented via a web-based platform. The methodology includes data collection from Roboflow.com (3,610 images of chili leaves across four classes: spots, curling, yellowing, and healthy), data preprocessing, augmentation, model training, and evaluation. The results demonstrate that the DenseNet169 model achieves an accuracy of 98%, with consistent precision, recall, and *F1-score* values for each class. The model is integrated into a Flask-based web application, allowing users to upload images of chili leaves for disease prediction and treatment recommendations. This system is expected to assist farmers in early disease detection, thereby improving cultivation efficiency and reducing crop failure risks.
Prediction of Students’ Major Selection Using the Fuzzy SugenoMethod Based on Report Card Grades Raihan Anugrah Fakhri; Alda Cendekia Siregar; Syarifah Putri Agustini Alkadri
Jurnal Ragam Pengabdian Vol. 3 No. 1 (Spesial Issue) (2026): "Dharma Samudera"
Publisher : Lembaga Teewan Journal Solutions

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62710/h96zxc86

Abstract

This study aims to develop a student achievement prediction system based on the Fuzzy Sugeno method using average report card scores to support science or social studies specialization recommendations. The data were obtained from student report cards at SMA 1 Toho in Microsoft Excel format and processed through fuzzification, inference, and defuzzification stages using triangular membership functions to produce crisp values and achievement categories. The system was implemented using the Python programming language and evaluated for result consistency. The findings indicate that the Fuzzy Sugeno method can objectively predict student achievement and support data-driven decision making in determining student specialization
Perencanaan Perjalanan Wisata Kabupaten Sambas Menggunakan Algoritma Ant Colony Optimization Maulidawati Maulidawati; Alda Cendekia Siregar; Rachmat Wahid Saleh Insani
Techno.Com Vol. 25 No. 2 (2026): May 2026
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v25i2.15806

Abstract

Sebuah perjalanan perlu dilakukan perencanaan agar waktu kunjungan dapat terkelola, salah satunya yaitu kunjungan wisata. Banyaknya wisata yang terdapat di Indonesia masing-masing memiliki daya tarik tersendiri. Salah satunya adalah wisata yang ada di Kabupaten Sambas. Minimnya informasi mengenai lokasi destinasi wisata di daerah tersebut menjadi salah satu kendala yang dialami wisatawan. Tujuan dari penelitian ini yaitu mencari rute terpendek berdasarkan nilai parameter optimal. Dalam permasalahan ini, algoritma yang digunakan yaitu Ant Colony Optimization sebagai algoritma optimasi yang dapat diterapkan pada kasus penentuan rute kunjungan wisata. Algoritma tersebut merupakan algoritma yang terinspirasi dari kelompok koloni semut dalam mencari jalur untuk mendapatkan makanan. Pengujian parameter Ant Colony Optimization yaitu alpha, beta, dan rho dengan rentang nilai yang telah ditentukan. Kemudian dibandingkan berdasarkan rata-rata fitness terkecil dari hasil 75 kali percobaan running. Hasil dari pengujian algoritma Ant Colony Optimization didapatkan nilai optimal parameter, yaitu alpha bernilai 0,02 dengan rata-rata fitness 315,56 km, beta bernilai 9,0 dengan rata-rata fitness 314,31 km, dan rho bernilai 0,5 dengan rata-rata fitness 316,01 km. Setelah didapatkan nilai parameter optimal tersebut, kemudian diujikan kembali dengan dilakukan percobaan running sebanyak 5 kali, jadi total keseluruhan running sebanyak 80 kali. Didapatkan fitness terkecil senilai 312,67 km, nilai fitness terbesar senilai 318,32 km, dan rata-rata fitness sebesar 315,28 km.   Kata kunci - optimasi, Ant Colony Optimization, rute terpendek, perencanaan perjalanan
Metode Analytical Hierarchy Process (AHP) untuk Pemilihan Media Sosial Pemasaran Songket Sambas Wawan Setiawan; Alda Cendekia Siregar; Asrul Abdullah
JUSIFO : Jurnal Sistem Informasi Vol 7 No 1 (2021): June
Publisher : Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Islam Negeri Raden Fatah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19109/jusifo.v7i1.7717

Abstract

Songket Sambas (Kain Lunggi) is a gold thread cloth typical of the culture of the Sambas people. The Sambas songket craftsmen have used social media a lot in marketing their products. The selection of the right social media in product marketing can help increase sales of Sambas songket. It can be the key to success in expanding Sambas songket sales to various regions. This study aims to apply the Analytical Hierarchy Process (AHP) method in the selection of social media that is widely used as the right Sambas songket marketing media. In this study, the AHP method was used as a method for decision making. In determining the pairwise comparison matrix, Sambas songket craftsmen were involved. The criteria used are age range (C1), gender (C2), number of users (C3), level of popularity (C4). From this study, we have the value of Facebook is 0.82, Instagram is 0.60, Youtube is 0.06, Twitter is 0.04. The final priority result of the right social media to use with the first order is Facebook.
Pemberdayaan Pendidik Di Kalimantan Barat Dalam Menghadapi Kecerdasan Buatan di Era Disruptif Alda Cendekia Siregar; Sonjoruri Budiani Trisakti
Jurnal Medika: Medika Vol. 4 No. 3 (2025)
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/2nsyyg84

Abstract

Perkembangan teknologi kecerdasan buatan (AI) telah membawa perubahan besar dalam dunia pendidikan, namun masih banyak pendidik di wilayah seperti Singkawang, Kalimantan Barat, yang belum siap menghadapi tantangan ini. Rendahnya literasi digital, minimnya pelatihan AI, serta keterbatasan infrastruktur menjadi hambatan utama dalam pemanfaatan AI oleh guru-guru setempat. Kegiatan pengabdian ini bertujuan untuk memberdayakan pendidik melalui pelatihan dan pendampingan penggunaan tools AI seperti ChatGPT dan Dreamina dalam kegiatan pembelajaran. Metode pelaksanaan mencakup observasi lapangan, workshop intensif, praktik langsung, serta evaluasi berbasis pretest dan posttest. Sebanyak 121 guru dari empat kabupaten mengikuti kegiatan ini, dengan hasil evaluasi menunjukkan peningkatan signifikan pemahaman peserta terhadap AI. Rata-rata skor posttest mencapai 88,71%, dengan mayoritas peserta menunjukkan pemahaman yang tepat terhadap konsep dan penerapan AI dalam pendidikan. Temuan ini menunjukkan bahwa pelatihan praktis dan pendampingan dapat secara efektif meningkatkan literasi AI guru, sekaligus menjembatani kesenjangan digital antara sekolah di perkotaan dan pedesaan. Hasil kegiatan ini menjadi dasar penting untuk pengembangan pelatihan lanjutan dan pembentukan komunitas guru AI di Kalimantan Barat guna menciptakan ekosistem pendidikan yang adaptif dan berkelanjutan.
Co-Authors Abdurrahman Abdurrahman Anastasya Putri Sulistio Angga Prima Syahputra Anisa Anisa Anugrah, Ilham Arsita Asrul Abdullah azwar, agim aljanata Barry Ceasar Octariadi Cahyo, Rahandya Chairul Syafar Putra Dedy Trianto Dewi Amelia Dian Hafidh Zulfikar Dila Adellia Eka Wahyuni Eki Komariah Fakhruzi, Izhan G. Gunawan Imanda, Imanda Istikoma Istikoma Lea Candra Maulidawati Maulidawati Menur Wahyu Pangestika Mi'andri Mi'andri Muhammad Agus Muljanto Mustafa Mustafa Nasim, Muhammad Nesa, Fathul Hakim Octariadi, Barry Ceasar Oktariadi, Barry Ceasar Poetro, Bagus Satrio Waluyo PRAYOGI, ADITYA Putri Nabila Aprilia Putri Yuli Utami Rabbil Budiman Rachmat Wahid Saleh Insani Rahimah, Auliyya Raihan Anugrah Fakhri Ramdhania, Nur Annisa Rodibelle F Leona Setiarini, Putri Rizka Siti Putri Anggrayeni Sonjoruri Budiani Trisakti Sucipto Sutarti Dwi Yanti Syarifah Putri Agustini Alkadri Wahid Saleh Insani, Rachmad Wawan Setiawan Widiasari, Putu Puja Diva Yanto, Maryogi Yulia, Rantika