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All Journal JURNAL SISTEM INFORMASI BISNIS MediaTor: Jurnal Komunikasi Jurnal Simetris Mediator Bulletin of Electrical Engineering and Informatics Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Pembangunan Pedesaan Journal of Information Systems Engineering and Business Intelligence Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Register: Jurnal Ilmiah Teknologi Sistem Informasi KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Science and Technology Indonesia JOIV : International Journal on Informatics Visualization INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Jurnal Komputasi JOURNAL OF APPLIED INFORMATICS AND COMPUTING ComTech: Computer, Mathematics and Engineering Applications Jurnal Pengabdian Kepada Masyarakat (JPKM) Tabikpun Buguh: Jurnal Pengabdian Kepada Masyarakat International Journal of Electronics and Communications Systems Jurnal Pepadun SWARNA: Jurnal Pengabdian Kepada Masyarakat JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Integra: Journal of Integrated Mathematics and Computer Science Jurnal Komputasi
Akmal Junaidi
Jurusan Ilmu Komputer Universitas Lampung
Religion Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Physics Social Sciences Other

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Perbandingan Kinerja Backpropagation dan Convolutional Neural Network untuk Klasifikasi Citra Batik Lampung Renada Dhea Armelia; Rico Andrian; Akmal Junaidi
Jurnal Komputasi Vol. 12 No. 1 (2024): Jurnal Komputasi
Publisher : Jurusan Ilmu Komputer Fakultas MIPA Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/komputasi.v12i1.248

Abstract

one of Indonesia's cultures on October 2, 2009. Lampung initially did not have a batik tradition but there is a legacy that is referred to as the first batik worn by Lampung people, namely sembagi cloth. Batik Siger is a business that produces batik typical of Lampung which originated from a course and training institution and was established in 2008. LKP Batik Siger provides services to the community in the field of written batik. This research discusses the performance of backpropagation and convolutional neural networks that will be used for the classification of Lampung batik image patterns. The Lampung batik motifs used are sembagi, pakjimo, granitan, soga, siger tangkup betik, jung agung, kembang cengkih and siger ratu agung. The stages that will be carried out are scaling, grayscale, thresholding and classification. The comparison of training data, testing data and validation used is 70:20:10 with the needs of backpropagation and convolutional neural network, namely epoch = 100, learning rate = 0.01. Backpropagation classification resulted in an accuracy of 96.25% and a classification error of 3.75%. The convolutional neural network classification resulted in an accuracy of 99.37% and a classification error of 0.63%. The performance of the CNN method has 3.12% higher accuracy compared to the performance of convolutional neural network.
Implementasi Metode Deep Learning Untuk Klasifikasi Gambar Tulisan Tangan Edi Arif Effendi; Favorisen Rosyking Lumbanraja; Akmal Junaidi; Admi Syarif
Jurnal Pepadun Vol. 4 No. 2 (2023): August
Publisher : Department of Computer Science, Faculty of Mathematics and Natural Sciences, University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/pepadun.v4i2.166

Abstract

The advancement of current technology has led to the widespread utilization of pattern recognition in diverse fields, such as identifying signature patterns, fingerprints, faces, and handwriting. Human handwriting exhibits variations from one person to another, often making it challenging to read or recognize, which can hinder daily activities, particularly in transactions requiring handwritten input. Handwriting, being a distinct expression of individuals, can be effectively distinguished or recognized using pattern recognition methods, particularly through computer-based classification techniques, including deep learning. In this research, deep learning was employed for the classification of handwritten characters, encompassing a total of 5200 data samples, consisting of lowercase and uppercase letters from 'a' to 'z,' with each letter represented by 100 data samples. The data underwent several stages, including pre-processing, feature extraction, classification, and evaluation. The evaluation phase employed k-fold cross-validation repeated ten times, which is a statistical technique aimed at assessing classifier performance. The study revealed that the highest accuracy, at 58.36%, was achieved using a 2-layer architecture with 512 and 256 units, while the lowest accuracy, at 43.42%, was obtained with a 5-layer architecture comprising 512, 256, 128, 64, and 32 units.
YOLOv5s for Traffic Prohibition Sign Detection in Bandar Lampung: An Empirical Evaluation Under Real-World Urban Conditions Sholehurrohman, Ridho; Junaidi, Akmal; Dewi, Tasya Nursita; Andrian, Rico; Ilman, Igit Sabda; Reza Habibi, Mohammad
International Journal of Electronics and Communications Systems Vol. 6 No. 1 (2026): International Journal of Electronics and Communications System
Publisher : Universitas Islam Negeri Raden Intan Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ijecs.v6i1.28786

Abstract

The detection of traffic prohibition signs in tropical urban environments is under-documented, as existing benchmark datasets such as GTSRB and TT100K do not represent the specific conditions of Southeast Asia. This study evaluates YOLOv5s for detecting and classifying six classes of traffic prohibition signs on four urban roads in Bandar Lampung, Indonesia, using a dataset of 9,898 labeled images extracted from real-world video recordings under various environmental conditions. YOLOv5s was directly compared with YOLOv4, YOLOv5m, and Faster R-CNN under identical evaluation conditions. YOLOv5s outperformed all comparison models with an average accuracy of 93.34% and an average F1-Score of 95.97%, with performance ranging from 88.65% in Pagar Alam to 97.28% at Unila, reflecting the documented gradation of environmental complexity. Processing speeds of 7.3–8.8 FPS place the system in the near-real-time category, making it suitable for offline traffic monitoring applications. This study provides a method for detecting prohibition signs in tropical urban environments in Indonesia and offers a practical reference point for the development of intelligent transportation systems in developing cities facing similar environmental challenges.
Word Stemming of Lampung Dialect Nyo using N-Gram Stemming Parjito Parjito; Zaenal Abidin; Akmal Junaidi; Wamiliana Wamiliana; Favorisen R. Lumbanraja; Farida Ariyani
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 10 No 1 (2026)
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v10i1.25364

Abstract

Background: Previous translation systems for the Lampung dialect of nyo to Indonesian achieved bilingual evaluation understudy (BLEU) scores below 40%, primarily due to challenges in processing affixed words. Objective: This research aims to perform stemming on affixed words in the Lampung dialect of nyo to enhance the performance of the translation system. Methods: We developed an n-gram stemming approach that reduces affixed words to their base forms by measuring similarity between n-grams using the Dice coefficient method. When similarity exceeds a specified threshold, the system identifies the corresponding base word. Results: Using a dataset of 700 words from the Lampung dialect of nyo, we constructed a comprehensive stemmer covering all affix variations. The optimal threshold was determined to be 0.5, achieving bigram accuracy of 93.86% and trigram accuracy of 89.14%. These accuracy levels demonstrate the method's effectiveness in identifying base word forms, which directly impacts translation quality improvement. Conclusion: N-gram stemming with a 0.5 threshold effectively processes the Lampung dialect of nyo morphology and shows potential for enhancing translation accuracy. This work represents the first comprehensive stemming system specifically designed for the Lampung dialect of nyo, contributing to the development of natural language processing tools for underrepresented regional languages in Indonesia. 
Essential Gene Classification in Drosophila melanogaster Using Genomic Signal Processing and Boosting Putri, Gendis Ananda; Lumbanraja, Favorisen Rosyking; Junaidi, Akmal; Aristoteles; Tristiyanto
International Journal of Electronics and Communications Systems Vol. 6 No. 1 (2026): International Journal of Electronics and Communications System
Publisher : Universitas Islam Negeri Raden Intan Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ijecs.v6i1.31239

Abstract

This study evaluates the efficacy of AdaBoost and XGBoost in classifying Cellular Essential Genes (CEG) and Organismal Essential Genes (OEG) of Drosophila melanogaster using a hybrid feature set of DNA sequences, protein sequences, and network topology 185 features comprising Tri-Nucleotide Composition (TNC, from DNA) and Fourier Transform (FT, from DNA only), Amino Acid Composition (AAC, from protein sequences), and Protein-Protein Interaction (PPI) degree (from network topology) retrieved from the CLEARER database, with Random Forest Gini feature selection and SMOTETomek balancing nested within a leakage-free stratified 5×10-fold cross-validation pipeline, demonstrating that XGBoost consistently outperforms AdaBoost by achieving 96.88% accuracy, 0.864 F1-score, and 0.845 MCC on the CEG hold-out test set, while sequence-derived features (TNC and AAC) emerge as the dominant predictors. Sequence-based features (TNC and AAC) dominated the selected feature set, with FT features accounting for 18 of the 45 selected features, confirming the value of genomic spectral signal processing as a complement to compositional representation. Overall, this study demonstrates the value of integrating genomic signal processing with boosting-based learning and provides a reproducible, leakage-controlled framework for essential gene classification that can inform future cross-organism prediction studies.
Co-Authors - Damayanti . Wamiliana Admi Syarif Ahmad Ari Aldino Ahmad Faisol Akbar, Mohammed Raihan Albertus Sudirman Alfikri, Fadli Andrian, Rico Ani Kurniawati Arif Munandar Arif Pebriansyah Aristoteles Asmiati Asmiati Ayu Amalia Ayu Nadila Bambang Hermanto Beni Adi Pranata Damayanti Damayanti Dewi, Tasya Nursita Dwi Sakethi Dwi Sakethi Dwi Sakethi Edi Arif Effendi Fajriyanto Fajriyanto Farida Ariyani Fazri, Yudistira Febi Eka Febriansyah Fitriani Gamal, Mohammad Danil Hendry Hamim Sudarsono . Heningtyas, Yunda Herindri Samodera Utami, Bernadhita Hidayat Pujisiswanto Hijriani, Astria Ida Nurhaida1 Ida Nurhaida Ida Nurhaida Ilman, Igit Sabda Indah Mayatika Sihaloho Innaya, Thalia Gemi Irawati, Anie Rose Irwan Adi Pribadi Irwan Adi Pribadi Iva Mutiara Indah Kartika Sari Kenny Claudie Fandau Kurnia Muludi Leila Fauziah Lucia Ratnasari Lumbanraja, Favorisen R Machudor Yusman Machudor Yusman Maharani, Devi Manurung, Yunita Rosalina Megawaty, Dyah Ayu Meizano Ardhi Muhammad Muhammad Iqbal Muhammad Jamaludin Muhammad, Meizano Ardhi Mustofa Usman Naurah Nazhifah Nikken Prima Puspita Nirwana Hendrastuty Nova Ayu Lestari Siahaan Novita Dwilestari Nugroho Susanto, Gregorius Nurdin, Muhaymi Ossy Dwi Endah Wulansari Ossy Dwi Endah Wulansari, Ossy Dwi Endah Pairul Syah Pairulsyah Pairulsyah Parjito Parjito Prabowo, Rizky Pranata, Beni Adi Putri, Gendis Ananda Rahmat Safe'i Rahmi Permata Hati Rangga Agustiantino Rangga Firdaus Renada Dhea Armelia Rendi Adam Rendi Eko Prasatiawan Reni Permata Sari Reza Aji Saputra Reza Habibi, Mohammad Rizky Ramadhany Rosdiana, Siti Rusdi Evizal S Susiyani Saiful Anwar Saipul Anwar Saipul Anwar Samsul Bakri Shofiana, Dewi Asiah Sholehurrohman, Ridho Siti Khabibah Siti Rosdiana Slamet Budi Yuwono Sugaluh Yulianti Sugeng P Hariyanto Sugeng P Hariyanto Sugeng Prayitno Hariyanto Susanto, Gregorius Nugroho Sutyarso Sutyarso Sutyarso, - Syachrul Priyo Wibowo Syifa Trianingsih Taqwan Thamrin Tio Arisandi Titik Nur Aeny Trianingsih, Syifa Tristiyanto Tsani, Machya Kartika Wamiliana Warsono Warsono Wartariyus Wartariyus Wiwin Susanty Wulan Kurnia Safitri Yoannisa Egeustin YOHANA TRI UTAMI, YOHANA TRI Yusikania Dwi Putri Zaenal Abidin Zaenal Abidin Zaini, TM