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All Journal International Journal of Electrical and Computer Engineering TEKNIK INFORMATIKA Jurnal Simetris Bulletin of Electrical Engineering and Informatics Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Jurnal Pengabdian Kepada Masyarakat Sakai Sambayan Jurnas Nasional Teknologi dan Sistem Informasi WARTA 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 Jurnal Teknoinfo Jurnal Tekno Kompak Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Pengabdian Kepada Masyarakat (JPKM) Tabikpun Journal of Applied Data Sciences Buguh: Jurnal Pengabdian Kepada Masyarakat International Journal of Electronics and Communications Systems Jurnal Pepadun Journal of Data Science and Software Engineering Warta LPM Laporan Upaya Nyata Inovasi Ilmu Komputer (LUNIK) Integra: Journal of Integrated Mathematics and Computer Science Jurnal Komputasi
Lumbanraja, Favorisen R
Jurusan Ilmu Komputer, Fakultas Matematika Dan Ilmu Pengetahuan Alam, Universitas Lampung
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry 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 Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Physics Social Sciences Other

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Pengembangan Sistem Rekruitmen Karyawan Perusahaan Mitra UPT Kewirausahaan Dan Pengembangan Karir Universitas Lampung Destian ade anggi Sukma; Machudor Yusman; Favorisen Lumbanraja; Rico Andrian
Jurnal Komputasi Vol. 8 No. 1 (2020)
Publisher : Jurusan Ilmu Komputer Fakultas MIPA Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/komputasi.v8i1.2331

Abstract

Recruitment is the process of finding and the best-qualified candidate work in a company or agency. There are various recruitment methods such as via employee recommendations, university collaboration, job vacancy, and jobsfair. In this paper an online company employee recruitment will be made using the black box testing method with the Equivalence Partitioning technique and the Likert scale. The data is taken from company users and job seekers. System displays job vacancies in accordance with the minimum level of education, gender and applicant's GPA. Job seekers fill out the Curriculum Vitae (CV) on the system as a company assessment for acceptance of applicants.The system can also provide announcements for applicants who have successfully passed a company. The system has been tested with black box testing with technique Equivalence Partitioning and get valid results for each test case, and for testing using a Likert scale gets very good results with a value of 87.05%.
IMPLEMENTASI SUPPORT VECTOR MACHINE (SVM) UNTUK KLASIFIKASI PEDERITA DIABETES MELLITUS Favorisen R Lumbanraja; Fanni Lufiana; Yunda Heningtyas; Kurnia Muludi
Jurnal Komputasi Vol. 10 No. 1 (2022)
Publisher : Jurusan Ilmu Komputer Fakultas MIPA Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/komputasi.v10i1.2940

Abstract

Diabetes Mellitus (DM) is a chronic disease characterized by the body's inability to metabolize carbohydrates, fats, and proteins, resulting in increased blood sugar (hyperglycemia) due to low insulin levels. Diabetes is due to a combination of heredity (genetics) and unhealthy lifestyles. Hemoglobin A1c is a blood test used to diagnose and manage diabetes patients when measuring blood sugar levels. This study aims to analyze predictive models for the classification of people with diabetes using R Shiny and evaluate the results of the support vector machine method's classification performance. There are many ways to diagnose diabetes, and the support vector machine is one of the machine learning algorithms used in this study's classification case (SVM). This study uses data from Diabetes 130-US Hospital For Years 1999-2008, which was sourced from the UCI Machine Learning Repository and consists of 34 variables and 84900 records, with dataset distribution and testing techniques using the 10-fold cross-validation method and three kernels in modeling using SVM, namely linear, Gaussian, and polynomial. The results obtained are a simple predictive model analysis system for classifying people with diabetes with shiny, making it easier for users to find out the prediction results and obtain the highest accuracy result, which is 82.76 percent of the gaussian kernel.
Performance Evaluation of Support Vector Machine (SVM) and XGBoost for Predicting Toddlers’ Stunting Status Based on Anthropometric Data Nurjoko Nurjoko; Admi Syarif; Favorisen R. Lumbanraja; Khairunisa Berawi
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1260

Abstract

Stunting remains a primary global health concern, particularly in developing countries, due to its long-term effects on physical growth, cognitive development, and overall well-being. Despite various public health initiatives, challenges in early detection persist, highlighting the need for accurate, data-driven predictive models to support targeted interventions. This study aims to develop and compare the performance of two machine learning algorithms—SVM and Extreme Gradient Boosting (XGBoost)—for classifying stunting status among children under five, in order to determine the most effective method for early prediction. A quantitative machine learning approach was applied to a dataset comprising 17,498 records derived from Posyandu data in Lampung Province, Indonesia. The analytical pipeline included data preprocessing, class rebalancing using the Synthetic Minority Over-sampling Technique (SMOTE), and model evaluation through stratified 10-fold cross-validation. Performance was assessed using accuracy, precision, recall, and F1-score. The XGBoost model demonstrated superior performance with accuracy, precision, recall, and F1-score reaching 0.9979. In comparison, the SVM model produced slightly lower yet still strong results, achieving an accuracy of 0.9949, with similarly consistent performance across other evaluation metrics. These findings indicate that XGBoost more effectively handles high-dimensional, imbalanced data and captures nonlinear patterns in the dataset. XGBoost was identified as the optimal method for stunting classification in this study, outperforming SVM across all evaluation metrics. These results support the integration of boosting-based models into early detection systems for child nutritional assessment. Future studies should incorporate additional environmental and socioeconomic variables and evaluate model applicability in a real-time community health setting.
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.
Sistem Web Real Time untuk Pelacakan Lokasi Pedagang Keliling Raka Akbar Hartolo; Febi Eka Febriansyah; Irwan Adi Pribadi; Favorisen Rosyking Lumbanraja
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.v4i2.173

Abstract

The rapid progress in technology, particularly in the realm of information technology, has significantly impacted various aspects of daily life. Despite these advancements, many street vendors continue to conduct trade within housing complexes or on smaller streets. This presents a challenge for potential buyers who find it inconvenient to purchase goods from street vendors due to uncertainties about their presence and the types of merchandise they offer. To address this issue, a viable solution is proposed: the development of a tracking system that utilizes GPS technology on smartphones to monitor the movements of street vendors. The location data of these vendors is transmitted to the internet, where it is converted into accessible information through a web interface built with the Codeigniter framework. Black Box testing confirms the effective functionality of the system, and the evaluation yields a score of 82.28%, indicating its successful implementation.
Two-Stage Convolutional Neural Network (CNN) Architectures for Breast Cancer Image Classification Admi Syarif; Adinda Aulia Sari; Wartariyus Wartariyus; Favorisen Rosyking Lumbanraja; Apri Candra
Jurnal Pepadun Vol. 6 No. 3 (2025): December
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.v6i3.292

Abstract

Breast cancer remains one of the most common and deadly diseases among women globally. Early detection significantly increases the chances of patient recovery. The main objective of this research is to evaluate the performance of three Convolutional Neural Network (CNN) architectures, namely ResNet50, VGG16, and DenseNet201, for breast cancer image classification. In this study, there are two classification stages used: the first is to differentiate between normal and abnormal images, and the second is to distinguish between benign and malignant tumors. The dataset was obtained through the Kaggle website. It was then pre-processed using normalization and augmentation through flipping and rotation. After each CNN model was trained using transfer learning, its performance was evaluated using accuracy, precision, recall, and F1 score. In the Normal and Abnormal classification task, the DenseNet201 model outperformed other models with an accuracy of 91%. Meanwhile, ResNet50 showed the most optimal results in the Benign and Malignant classification with an accuracy of 83%.
Classification of Public Sentiment towards the Performance of the Ministry of Communication and Digital regarding Online Gambling Ika Rahma Alia; Favorisen Rosyking Lumbanraja; Aristoteles Aristoteles; Rico Andrian
Jurnal Pepadun Vol. 6 No. 3 (2025): December
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.v6i3.295

Abstract

Online gambling is a social issue currently in the spotlight in Indonesia. Although the government, particularly the Ministry of Communication and Digital (Kemkomdigi), has taken various measures, such as blocking websites and conducting digital literacy campaigns, online gambling remains rampant and has sparked various public reactions. Social media, particularly Instagram, has become a public space where people express their opinions and sentiments regarding government performance. This study aims to classify public sentiment based on comments directed at the official Kemkomdigi Instagram account regarding the issue of online gambling. This study uses two machine learning algorithms, Random Forest and XGBoost, to compare the effectiveness of the models in classifying positive and negative sentiment. A total of 724 comments were collected and manually labeled by three annotators using a voting method. Preprocessing included cleaning, case folding, tokenization, normalization, stopword removal, and stemming. Feature representation was performed using the TF-IDF method. The data was split with a 70:30 ratio and balanced using Random Oversampling. Model training used 10-fold cross-validation and hyperparameter tuning through GridSearchCV. The evaluation results showed that the tuned Random Forest performed the best, with an accuracy of 0.7082. These findings demonstrate that machine learning approaches, particularly Random Forest, are effective in automatically identifying public sentiment toward emerging public policy issues on social media.
Enhancing Low-Resource Lampung Speech Recognition through Cross-Lingual XLSR-Wav2Vec 2.0 Pretraining Hendra Kurniawan; Akmal Junaidi; Favorisen Rosyking Lumbanraja; Wamiliana Wamiliana
Journal of Applied Data Sciences Vol 7, No 3: September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i3.1388

Abstract

This study investigates the application of Wav2Vec 2.0 (W2V2) and Cross-Lingual Speech Representation (XLSR) models to Lampung language speech recognition. LampungNyow v1.0 is introduced, a speech corpus designed to provide a baseline for training and evaluating Automatic Speech Recognition (ASR) for this low-resource regional language of Indonesia. The dataset enables supervised fine-tuning and standardized evaluation, addressing the lack of publicly available linguistic resources for Lampung. Several pre-trained W2V2 models on Lampung speech recognition using Word Error Rate (WER) as the evaluation metric. The evaluated models include W2V2-Base, W2V2-Large, W2V2-Large-XLSR-Indonesian, W2V2-Large-XLSR-Sundanese, W2V2-Large-XLSR-53, and the multilingual W2V2-Large-XLSR-Indonesia-Javanese-Sundanese model. Monolingual models have higher WER values, according to experimental results: W2V2-Base achieved 36,23%, while W2V2-Large achieved 36,30%. XLSR models, such as XLSR-53 (33,88%), Sundanese (33,99%), and Indonesian (33,70%), demonstrated modest improvements. The W2V2-Large-XLSR-Indonesian-Javanese-Sundanese model, which was the foundation for the Lampung automatic speech recognition system in this study, achieved lower WER of 17,39%. These findings suggest that, in contrast to more comprehensive multilingual or monolingual pretraining models, multilingual pretraining utilizing a number of Indonesian regional languages can produce acoustic and contextual speech representations that are better suited for the resource-constrained Lampung automatic speech recognition task. When compared to the baseline W2V2-Large model, the obtained WER of 17,39% indicates a relative improvement of more than 50%.
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. 
PELATIHAN PENGGUNAAN MICROSOFT WORD UNTUK GURU SEKOLAH DASAR DI SDN MARGOREJO, KEC. JATI AGUNG, KABUPATEN LAMPUNG SELATAN Favorisen R. Lumbanraja; Ridho Sholehurrohman; M. Iqbal Parabi; Igit Sabda Ilman; Muhaqiqin Muhaqiqin
Laporan Upaya Nyata Inovasi Ilmu Komputer JPKM Lunik - Vol 02, No. 01, April 2024
Publisher : FMIPA Unila

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/lunik.v2i01.23

Abstract

Kegiatan pelatihan Microsoft Word ini dirancang untuk memberikan peserta pemahaman mendalam dan keterampilan praktis dalam menggunakan perangkat lunak pengolah kata Microsoft Word secara efisien dan efektif. Pelatihan akan mencakup penguasaan fitur dasar hingga tingkat lanjutan, termasuk penggunaan gaya dokumen, tata letak halaman, objek visual, mail merge, dan kolaborasi dalam pengeditan dokumen bersama. Tujuan utama pelatihan ini adalah meningkatkan produktivitas dan kualitas kerja peserta dengan memanfaatkan potensi penuh Microsoft Word. Peserta akan diajarkan teknik-teknik efisien, pintasan keyboard, dan fitur-fitur otomatisasi untuk menghemat waktu dalam pembuatan dan penyuntingan dokumen. Mereka akan belajar tentang penggunaan gaya dokumen untuk memastikan konsistensi tampilan dan format, serta bagaimana mengatur objek visual seperti gambar, tabel, dan grafik. Selain itu, pelatihan akan membantu peserta mengatasi tantangan dalam kolaborasi tim dengan pengenalan alat kolaborasi dalam Microsoft Word. Peserta juga akan memahami teknik penggabungan data melalui mail merge dan penerapan penggunaan template untuk berbagai jenis dokumen.Pelatihan ini akan memberikan manfaat nyata dalam dunia kerja dan pendidikan. Peserta akan mampu menghasilkan dokumen berkualitas tinggi yang memenuhi standar profesional, mengurangi waktu yang dihabiskan untuk tugas pengolahan kata, dan meningkatkan efisiensi dalam proses kerja. Selain itu, peserta akan memiliki kemampuan yang relevan dengan tuntutan pekerjaan dan pendidikan saat ini, memberi mereka keunggulan kompetitif dalam berbagai situasi. Melalui kegiatan pelatihan ini, peserta akan memperoleh kepercayaan diri yang lebih tinggi dalam mengoperasikan Microsoft Word dan menghadapi tugas-tugas yang melibatkan pengolahan kata. Pelatihan ini bukan hanya investasi dalam keterampilan teknis, tetapi juga investasi dalam pengembangan diri yang akan membawa manfaat jangka panjang dalam karir dan kehidupan sehari-hari. Kegiatan PKM ini bertujuan untuk melakukan transfer pengetahuan dalam mengimplementasikan aplikasi MS.Word sebagai wujud menjadi masyarakat unggul dan guru penggerak.
Co-Authors - Damayanti . Wamiliana Adawiyah, Laila Adinda Aulia Sari Admi Syarif Admi Syarif Admi Syarif Aflaha Asri Ahyarudin AKBAR RISMAWAN TANJUNG Akbar, Mohammed Raihan Akmal Junaidi Akmal Junaidi Akmal Junaidi Amelia Jasmine Andrian, Rico Annisa Rizqiana Apri Candra Ardiansyah Ardiansyah Aristoteles Aristoteles, Aristoteles Asmiati Asmiati Aulia Putri Ariqa Ayu Amalia Bambang Hermanto Damayanti Damayanti Danu Sasmita Desti Fatmalasari Destian ade anggi Sukma Dian Kurniasari Didik Kurniawan Dwi Kartini, Dwi Dwi Sakethi Dwi Sakethi, Dwi Edi Arif Effendi Eliza Fitri Elly Lestari Rusitati Erdi Suroso Fanni Lufiana Fanni Lufiana Farida Ariyani Febi Eka Febriansyah Febi Eka Febriansyah Fitriyana, Silfia Hadi, Normi Abdul Hamim Sudarsono . Hamzah Hanif Hdiana, Yazid Zinedine Hendra Kurniawan Heningtyas, Yunda Hijriani, Astria Igit Sabda Ilman Ika Rahma Alia Indah Pasaribu Ira Hariati Br Sitepu Irawati, Anie Rose Irwan Adi Pribadi Jasmine, Amelia Jihan Aferiansyah Junaidi Junaidi Junaidi Junaidi Khairun Nisa Kristina Ademariana Kurnia Muludi Kurnia Muludi Kurnia Muludi Kurnia Muludi Lilies Handayani M. Iqbal Parabi M. Juandhika Rizky Machudor Yusman Manurung, Yunita Rosalina Megawaty, Dyah Ayu Meria Nensi Muhammad Reza Faisal, Muhammad Reza Muhammad Reza Habibi Muhammad Rizki Muhaqiqin Muhaqiqin Muliadi Mustofa Usman Nadila Rizqi Muttaqina Naurah Nazhifah Nirwana Hendrastuty Nova Ayu Lestari Siahaan Nugroho Susanto, Gregorius Nuning Nurcahyani Nurdin, Muhaymi Nurhasanah Nurhasanah Nurjoko Nurjoko Parjito Parjito Prabowo, Rizky Pratama, Rinaldo Adi Priyambodo Priyambodo Priyambodo Priyambodo Putri, Gendis Ananda Qory Aprilarita Rahmat Safe'i Raka Akbar Hartolo Rangga Agustiantino Reza Aji Saputra Rico Andrian Ridho Sholehurrohman Ridho Sholehurrohman RM Sulaiman Sani Rosdiana, Siti Rudy Herteno Rudy Herteno Rusitati, Elly Lestari Saragih, Triando Hamonangan Shofiana, Dewi Asiah Sintiya Paramitha Siti Aisyah Solechah Siti Rosdiana Su'admaji, Arif Susanto, Gregorius Nugroho Sutyarso Sutyarso Sutyarso, - Syangap Diningrat Sitompul TANJUNG, AKBAR RISMAWAN Tiyara Saghira Tristiyanto Tristiyanto Tristiyanto Wamiliana Wamiliana Wamiliana Warsono Warsono Warsono Warsono Warsono Wartariyus Wartariyus YOHANA TRI UTAMI, YOHANA TRI Zaenal Abidin Zuliana Nurfadlilah