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All Journal Indonesian Journal of Electronics and Instrumentation Systems ELKHA : Jurnal Teknik Elektro JUITA : Jurnal Informatika Annual Research Seminar Sinkron : Jurnal dan Penelitian Teknik Informatika Jusikom : Jurnal Sistem Komputer Musirawas SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Petir KOMPUTIKA - Jurnal Sistem Komputer Jurnal Teknologi Terpadu EDUMATIC: Jurnal Pendidikan Informatika Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Progresif: Jurnal Ilmiah Komputer Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer JATI (Jurnal Mahasiswa Teknik Informatika) Syntax: Journal of Software Engineering, Computer Science and Information Technology Algoritme Jurnal Mahasiswa Teknik Informatika Informatics and Enginering Dedication DEVICE : JOURNAL OF INFORMATION SYSTEM, COMPUTER SCIENCE AND INFORMATION TECHNOLOGY Jurnal Informatika dan Multimedia MDP Student Conference JRIIN :Jurnal Riset Informatika dan Inovasi Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Applied Information Technology and Computer Science (AICOMS) INOVTEK Polbeng - Seri Informatika JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Yohannes, Yohannes
Universitas Multi Data Palembang
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Pendekatan Dynamic Programming pada Penentuan Urutan Pembelajaran Optimal Berdasarkan Beban Kognitif Mahasiswa M Dhafa Adjie Saputra; Joseph Eduard Uly Loni; Yohannes
Jurnal Riset Informatika dan Inovasi Vol 4 No 1 (2026): JRIIN : Jurnal Riset Informatika dan Inovasi (INPRESS)
Publisher : shofanah Media Berkah

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Abstract

Penelitian ini bertujuan untuk mengoptimalkan prioritas belajar mahasiswa menjelang ujian akhir semester (UAS) dengan memformulasikan masalah pemilihan mata kuliah sebagai 0/1 knapsack problem. Data diperoleh dari dua dataset sintetis yang menggabungkan karakteristik mahasiswa (tingkat stres, kelelahan, efisiensi belajar, performa) dan mata kuliah (tingkat kesulitan, urgensi jadwal ujian) menjadi nilai prioritas (priority score) serta alokasi waktu belajar sebagai bobot. Eksperimen dilakukan pada 10 kapasitas waktu belajar berbeda (4–28 jam) untuk membandingkan algoritma Dynamic Programming (DP) dan Greedy. Hasil menunjukkan bahwa DP selalu menghasilkan solusi optimal atau superior dengan total nilai prioritas lebih tinggi atau sama dibandingkan Greedy pada seluruh kapasitas, dengan optimality gap tertinggi sebesar 9,87% pada kapasitas 16 jam. Meskipun waktu eksekusi DP lebih lambat (mikrodetik), perbedaannya tidak signifikan untuk ukuran data kecil. Disimpulkan bahwa pendekatan DP efektif untuk merekomendasikan kombinasi mata kuliah prioritas secara optimal. Penelitian selanjutnya disarankan menggunakan data nyata dan algoritma optimasi lain seperti branch and bound atau algoritma genetika.
Optimasi Rute Perjalanan Kunjungan Sekolah dengan Menggunakan Genetic Algorithm Veraldo; Klaudius Audie Irsansaputra; Yohannes
Jurnal Riset Informatika dan Inovasi Vol 4 No 1 (2026): JRIIN : Jurnal Riset Informatika dan Inovasi (INPRESS)
Publisher : shofanah Media Berkah

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Abstract

Penelitian ini bertujuan untuk mengoptimalkan rute perjalanan kunjungan sekolah di Kota Palembang menggunakan Algoritma Genetika berbasis data jaringan jalan nyata dari OpenStreetMap. Dataset yang digunakan terdiri dari 187 sekolah dengan titik awal perjalanan berada di Gedung Sudirman UMDP. Permasalahan dimodelkan sebagai Travelling Salesman Problem (TSP) dengan mempertimbangkan jaringan jalan berbentuk directed graph sehingga mampu merepresentasikan kondisi lalu lintas satu arah dan konektivitas jalan secara realistis. Untuk mengurangi kompleksitas ruang solusi, dilakukan strategi proximity-based clustering dengan membagi sekolah ke dalam beberapa kelompok berdasarkan kedekatan geografis sebelum proses optimasi dilakukan. Algoritma Genetika diimplementasikan menggunakan kombinasi Order Crossover (OX), swap mutation, dan seleksi elitis dengan parameter 50 generasi dan ukuran populasi sebanyak 20 individu. Hasil penelitian menunjukkan bahwa sistem berhasil memetakan seluruh sekolah ke dalam 37 rute kunjungan dengan total jarak akumulatif sebesar 648,51 km dan rata-rata 17,53 km per rute. Algoritma menunjukkan performa optimal dengan rata-rata pencarian solusi terbaik yang mengonvergi pada generasi ke-5. Penggunaan data jaringan jalan nyata memberikan representasi rute yang lebih akurat dibandingkan pendekatan jarak Euclidean karena mampu memperhitungkan aturan jalan satu arah dan konektivitas asimetris. Sistem yang dibangun berpotensi dikembangkan sebagai alat bantu pengambilan keputusan bagi instansi pendidikan dalam menyusun jadwal dan rute kunjungan sekolah secara lebih terstruktur, efisien, dan terdokumentasi.
Optimasi Rute Pengisian Daya Kendaraan Listrik SPKLU Jakarta Pusat Menggunakan Dynamic Programming: Optimizing Electric Vehicle Charging Routes at Central Jakarta's SPKLU Using Dynamic Programming Femmy Johan; Jennifer Verty; Yohannes Yohannes
Jurnal Informatika dan Multimedia Vol. 18 No. 1 (2026): Jurnal Informatika dan Multimedia
Publisher : Politeknik Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jtim.v18i1.10019

Abstract

The development of electric vehicles in Indonesia has increased the need for Public Electric Vehicle Charging Stations (SPKLU). Electric vehicle users often experience difficulties in determining the closest SPKLU location and in accordance with the current vehicle position. This study aims to create an optimization program for determining electric vehicle charging routes at SPKLU in the Central Jakarta area using the Dynamic Programming algorithm. Research data was obtained from Google Maps, including the SPKLU name, latitude, longitude, wattage, charger type, and address. Distance calculations were carried out using the Geodesic Distance method based on the coordinates of the vehicle and SPKLU locations. Next, reachable SPKLUs were evaluated using a cost function that combines travel distance and estimated charging time. The Dynamic Programming algorithm was used to determine the minimum cost value as the optimal solution, then the program generated a ranking of the best SPKLU recommendations. The study was built using the Python programming language and displayed visualizations of locations and routes to SPKLUs on a digital map using Folium. The results showed that the program was able to provide optimal SPKLU recommendations based on a combination of distance and charging time, thus helping electric vehicle users determine charging locations more effectively and efficiently.
Performance Analysis of YOLOv11 Integrated with Lightweight Backbones (MobileNetV2, GhostNet, ShuffleNet V2) for Cigarette Detection Kevin Andreas; Yohannes; Meiriyama
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/0gjq1j10

Abstract

Cigarette object detection in indoor environments plays a vital role for enforcing smoke-free zone regulations and protecting public health from secondhand smoke exposure. This study investigates the performance of YOLOv11n architecture integrated with three lightweight backbone modifications (MobileNetV2, GhostNet, and ShuffleNet V2) for real-time cigarette detection with the aim of achieving efficiency suitable for potential deployment on resource-constrained edge devices. Comprehensive experiments were conducted using the Cigar Detection Dataset comprising 5,333 images, augmented to 8,890 samples through horizontal flipping and brightness adjustment techniques. All models were trained for 100 epochs using the SGD optimizer on an NVIDIA Tesla T4 GPU. The evaluation metrics included detection accuracy (mAP@0.5, mAP@0.5:0.95, precision, recall, and F1-score) and computational efficiency (parameters, model size, GFLOPs, and FPS). Experimental results demonstrate that the pretrained YOLOv11n baseline achieves the highest detection accuracy with mAP@0.5 of 0.8072 and precision of 0.8688. Among lightweight backbone variants, ShuffleNet V2 (0.5x) provides the most compact solution with only 2.28M parameters and a 4.73 MB model size, while ShuffleNet V2 (0.75x) offers an optimal balance between accuracy (mAP@0.5: 0.7430) and efficiency with only 0.95% accuracy degradation compared to the 1.0x variant. These findings provide practical guidance for selecting appropriate model configurations based on deployment constraints in smoke-free area monitoring systems.
PENERAPAN METODE BRANCH AND BOUND DALAM OPTIMASI RUTE PENGIRIMAN PRODUK SKINCARE BERDASARKAN MINIMASI BIAYA OPERASIONAL Serenity Devina Suryanto; Albert Cahayadi; Yohannes Yohannes
Syntax : Journal of Software Engineering, Computer Science and Information Technology Vol 7, No 1 (2026): Juni 2026
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/syntax.v7i1.8948

Abstract

Skincare merupakan salah satu barang yang paling banyak didistribusikan. Distribusi produk skincare yang banyak juga membuat distributor mencari solusi dalam mencari rute pengiriman dengan guna meminimalkan biaya pengiriman. Permasalahan yang sering terjadi dalam proses distribusi adalah pemilihan rute pengiriman yang kurang optimal sehingga menyebabkan pengeluaran ekstra pada biaya transportasi. Rancangan sistem dalam upaya mencari rute optimal dengan biaya minimal menggunakan algoritma Branch and Bound dengan membatasi kemungkinan rute yang tidak memenuhi kriteria minimum sehingga proses pencarian menjadi lebih efisien. Data yang digunakan dalam penelitian meliputi Costs, Location, dan transportation modes pengiriman produk skincare. Penerapan ini menghasilkan cost minimum sebesar 772.40 dengan rute optimal pada transportation modes Air yang menjadi hasil yang paling efisien dibandingkan dengan transportion modes lain yaitu Road, Rail dan Sea. Berdasarkan hasil penelitian tersebut, dapat disimpulkan bahwa metode Branch and Bound mampu menghasilkan rute pengiriman yang lebih optimal dibandingkan metode konvensional, sehingga dapat mengurangi total biaya distribusi dan meningkatkan efisiensi proses pengiriman produk skincare. Dengan demikian, metode ini dapat dijadikan solusi dalam pengambilan keputusan untuk optimasi sistem distribusi pada perusahaan skincare.Kata Kunci— Branch and Bound , Optimasi pemilihan rute pengiriman, Skincare, Optimasi biaya. ABSTRACT Skincare is one of the most widely distributed products. The extensive distribution of skincare products also forces distributors to seek solutions in finding shipping routes to minimize shipping costs. A common problem in the distribution process is the selection of suboptimal shipping routes, which results in extra expenses on transportation costs. The system design in an effort to find the optimal route with minimal costs uses the Branch and Bound algorithm by limiting the possibility of routes that do not meet the minimum criteria so that the search process becomes more efficient. The data used in the study include Costs, Location, and transportation modes for skincare product delivery. This application produces a minimum cost of 772.40 with the optimal route in Air transportation modes being the most efficient result compared to other transportation modes, namely Road, Rail, and Sea. Based on the results of this study, it can be concluded that the Branch and Bound method is able to produce a more optimal shipping route than conventional methods, thereby reducing total distribution costs and increasing the efficiency of the skincare product delivery process. Thus, this method can be used as a solution in decision-making for optimizing the distribution system in skincare companies.Keywords— Branch and Bound , Delivery route optimization, Skincare, Cost Optimization.
PENERAPAN DYNAMIC PROGRAMMING PADA PENJADWALAN PENERBANGAN UNTUK MINIMASI DELAY Ariel Sudarsono; Raphael Lee; Yohannes Yohannes
Syntax : Journal of Software Engineering, Computer Science and Information Technology Vol 7, No 1 (2026): Juni 2026
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/syntax.v7i1.8911

Abstract

Abstrak— Keterlambatan penerbangan komersial menjadi tantangan besar dalam manajemen operasional bandara yang berdampak pada efisiensi sistem. Penelitian ini bertujuan mengoptimalkan penjadwalan penerbangan untuk meminimalkan total delay menggunakan algoritma Dynamic Programming. Eksperimen dilakukan menggunakan dataset maskapai penerbangan dari Kaggle dengan membatasi ruang lingkup pada 15.000 baris data pertama dan kapasitas waktu operasional harian sebesar 1.440 menit. Parameter weight ditentukan berdasarkan durasi terbang, sedangkan value dibentuk menggunakan fungsi penalti keterlambatan. Tahap post-processing diterapkan dengan aturan celah waktu minimum 20 menit untuk mengeliminasi konflik rute. Hasil penelitian menunjukkan algoritma berhasil menyusun kombinasi jadwal final berisi 16 penerbangan optimal. Penerapan metode ini terbukti efektif menekan total keterlambatan menjadi 107,0 menit dengan rata-rata delay sebesar 6,68 menit per penerbangan.Kata Kunci—Dynamic Programming, Keterlambatan Penerbangan, Optimasi Jadwal, Post-processing. AbstractCommercial flight delays present a major challenge in airport operational management, impacting system efficiency. This study aims to optimize flight scheduling to minimize total delays using Dynamic Programming. Experiments were conducted using an airline dataset from Kaggle, limiting the scope to the first 15,000 data rows with a daily operational time capacity of 1,440 minutes. The weight parameter was determined based on flight duration, while the value was formulated using a delay penalty function. A post-processing stage applied a minimum 20-minute time gap rule to eliminate route conflicts. The results show that the algorithm successfully generated a final schedule combination of 16 optimal flights. This approach effectively reduced the total delay to 107,0 minutes, achieving an average delay of only 6,68 minutes per flight.Keywords— Dynamic Programming, Flight Delays, Post-processing, Schedule Optimization.
Optimization of MobileNet Architecture with Ghost Module for Dental Enamel Caries Classification M. Zaky Naufal Farisky; Yohannes
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 11 No. 3 (2026): August (Inpress)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/ar0ts195

Abstract

Dental and oral diseases, particularly dental caries, represent a global health issue that requires early identification at the enamel stage to prevent further demineralization and damage. The utilization of artificial intelligence technology through Convolutional Neural Networks (CNNs) has been widely applied for medical image analysis. However, complex conventional models often incur high computational loads. Therefore, this study aims to implement and evaluate MobileNet variants (V1, V2, V3, and V4) optimized using the Ghost Module to classify dental caries images. The integration of the Ghost Module aims to mitigate feature redundancy and enhance feature representation without compromising image extraction quality. A dataset of 2,000 clinical dental images from the public "Caries-Spectra," categorized as advanced enamel caries, early-stage enamel caries, and no enamel caries, was curated and expanded to 12,000 images using preprocessing and augmentation. The dataset splitting was executed with a final learning proportion of 80% training data, 10% validation data, and 10% testing data. The image preprocessing utilized histogram equalization, CLAHE, and adaptive thresholding methods. The overall hybrid architecture was then evaluated based on performance metrics (such as accuracy, precision, recall, and F1 score). The experimental results demonstrate that the integration of the Ghost Module consistently enhances the classification accuracy across all MobileNet variants. The proposed Hybrid MobileNetV1 with Ghost Module achieved the highest performance, securing an overall accuracy of 96.83%, with well-balanced precision and recall across all enamel caries stages
DETEKSI PENYAKIT PADA TANAMAN CABAI BERBASIS DEEP LEARNING MENGGUNAKAN MODEL YOLOv11-L Candra Candra; Siska Devella; Yohannes Yohannes
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 10 No. 2 (2026): JATI Vol. 10 No. 2
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v10i2.17710

Abstract

Penyakit pada tanaman cabai sering menyebabkan terjadinya penurunan produktivitas yang signifikan bagi para petani. Deteksi dini merupakan salah satu langkah yang dapat diambil untuk mengantisipasi hal ini, namun jika dilakukan secara manual tentu memerlukan waktu lama. Penelitian ini bertujuan untuk mengembangkan model deteksi dini khususnya pada tanaman cabai menggunakan algoritma YOLOv11-L. Metode penelitian meliputi pengumpulan dataset sebanyak 450 citra tanaman cabai yang terdiri dari tanaman cabai sehat, antraknosa, daun bercak cokelat, layu fusarium, virus kuning, dan daun keriting dengan masing-masing data berjumlah 75 citra. Hasil terbaik dari pelatihan model deteksi memperoleh mAP@50 sebesar 64,42% dengan hasil pengujian menggunakan data uji sebanyak 45 citra memperoleh tingkat keberhasilan deteksi sebesar 93,33% dengan kesalahan sebesar 6,67%. Hasil ini menunjukkan bahwa model YOLOv11-L cukup efektif digunakan sebagai alat bantu yang dapat digunakan oleh para petani dalam mengidentifikasi kesehatan tanaman cabai dengan cepat untuk mencegah terjadinya gagal panen.
Feature Interaction and Performance Analysis of RankSum-Based Extractive Summarization in Indonesian Scientific Articles Verrino Adityya; Yohannes Yohannes
Jurnal Pendidikan Informatika (EDUMATIC) Vol 10 No 1 (2026): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v10i1.33443

Abstract

The extractive summarization of Indonesian scientific articles is hindered by a domain mismatch where established methodologies rely on news-corpus assumptions, whereas Indonesian scientific discourse follows rigid, IMRaD-driven structural and lexical patterns. This study aims to systematically analyze feature interaction effects and saturation behaviour in RankSum-based extractive summaries for Indonesian scientific articles. Designed as a controlled comparative experiment, this research evaluates a RankSum framework integrating variables, such as graph-based, semantic-thematic vectors, and structural heuristics. The dataset comprises 2,897 Indonesian journal articles (2021-2025) collected via web scraping from open-access university repositories. Analysis across 31 scenarios demonstrates that for Indonesian scientific articles, the assumption that increasing feature density improves performance is flawed, instead a feature saturation effect occurs. Results show that a 4-feature combination maximizes unigram lexical precision (ROUGE-1 0.3564), whereas the full 5-feature fusion is necessary to preserve global semantic integrity, structural flow, and stable (ROUGE-L 0.2018; BERTScore 0.6977). This study establishes a generalizable principle for domain-aware ATS by demonstrating that overcoming domain mismatch relies on navigating feature saturation through selection aligned with the document’s inherent logic rather than raw feature quantity.
Soybean Seed Quality Classification using Magnitude-Enhanced Multiple Channel LBP and SVM Adrian Chandra; Yohannes Yohannes
Jurnal Pendidikan Informatika (EDUMATIC) Vol 10 No 1 (2026): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v10i1.33519

Abstract

Soybean is a major source of plant-based protein The quality of soybean seed affects resulting food. Therefore, image processing for classifying soybean seed quality is needed. Previous studies mainly used handcrafted or deep learning features and not evaluated local texture representations that using magnitude information for multi-class problems with high visual similarity. Traditional texture descriptors such as LBP or GLCM mainly using sign-based or global statistics and have limitations in representing colour-texture variations. This study aims to classify soybean seed quality using SVM with Multiple Channel Local Binary Pattern (MCLBP) and its enhanced variant with magnitude information (MCLBP+M) for feature extraction by utilizing correlations between colour channels through multi-radius approach. The dataset used is Soybean Seeds includes five classes: intact, spotted, immature, broken, and skin-damaged. This research conduct dataset splitting using 10-fold cross validation, data balancing (SMOTE), feature extraction, SVM model training and testing, and performance evaluation. The results show that MCLBP+M with Lab colour space and RBF kernel achieves accuracy of 86.30%, precision of 86.32%, recall of 85.99%, and F1-score of 86.07%. The results show that magnitude information in MCLBP+M consistently stable and improves classification performance across colour spaces and kernels, making it suitable for soybean seed quality classification.
Co-Authors Ade Hendri Pandrean Adhytio Mahendra Adrian Chandra Albert Cahayadi Andreas, Kevin Ariel Sudarsono Azarya, Philips Denny Beni Anthony Bobby Jaya Saputra Cahyati, Imelia Dwinora Calvin Oliver Saputra Candra Candra Celvine Adi Putra Cendy Prakarsah Daffa Yudha Musyaffa Dafid Dafid Dandy, Dandy Daniel Udjulawa Daniel Udjulawa Devella, Siska Dody, Muhammad Fadhel Muhammad Famerdi, Farhan Agung Farhan Agung Famerdi Farisi, Ahmad Febbiola Febbiola Felix Gunawan Femmy Johan Feristyani, Indah Firda Novia Rahmawati Gerry Jeven Timoti Glen, Billy Hafiz Irsyad Hafiz Irsyad Hartati, Ery Inayatullah Inayatullah Indah Feristyani Jaysen Stephanus Jendraja Husin Kotan Jennifer Verty Jericho Jericho Jerry Setiawan Jimmy Aprilyanto Johannes Petrus Jonathan Tanujaya Joseph Eduard Uly Loni Julian Rusli Tee Baldi Juliana Nasution Kelvin Arianto Kevin Andreas Klaudius Audie Irsansaputra Laksana, Jovansa Putra Leo Chandra Leonardo Leonardo M Dhafa Adjie Saputra M. Zaky Naufal Farisky Marcella, Dewi Meiriyama Meiriyama, Meiriyama Migel Orvin Febryan Molavi Arman Muhammad Ezar Al Rivan Muhammad Farid Athar Muhammad Radja Juang Jamemiko Muhammad Rizky Pribadi Muhammad Yudha Setiawan Muhdhor, Umar Novan Wijaya Nur Rachmat Pandi Pandi Pandi Pandi, Pandi Pandrean, Ade Hendri Philips Denny Azarya Prabowo, Adrianus Prasthio, Rial Putra, Lipi Amanda Raphael Lee Ricky Wijaya RR. Ella Evrita Hestiandari Sahpira, Mulia Saputra, Dika Sari, Yulya Puspita Selvie Selvie Serenity Devina Suryanto Setiawan, Jerry Siska Amelia Siska Devella Siti Fatimah Az Zahrah Sonia Sonia, Sonia Tanuwijaya, William Timoteus Ivan Sariyo Veraldo Verrino Adityya Wijang Widhiarso William Hadisaputra William Tanuwijaya Yeremia Agung Chandra Yoannita Yoannita Yoannita Yoannita, Yoannita Yulya Puspita Sari