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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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Application of Random Forest Method Classification for Glycosylation in Lysine Protein Sequences Fitriyana, Silfia; Syarif, Admi; Rossyking, Favorisen; Faisal, Mohammad Reza
Integra: Journal of Integrated Mathematics and Computer Science Vol. 1 No. 2 (2024): July
Publisher : Magister Program of Mathematics, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/integrajimcs.20241218

Abstract

Grouping glycosylated lysine proteins into groups according to the type of glycosylation seen in the lysine protein sequence is known as glycosylation in the lysine protein sequence. In this work, the sensitivity, specificity, accuracy, and Matthew’s correlation coefficient (MCC) of the random forest approach for classifying the glycosylation of lysine protein sequences were examined. With 214 positive and 406 negative data, the lysine protein dataset derived from benchmark data contains 620 total proteins with a protein length of 15 sequences. 90% of the dataset is used for training, while 10% is used for testing. Using the R package BioSeqClass version 1.44.0, feature extraction employed protein descriptors, specifically AA Index, CTD, and PseAAC, with a total of 60 features. The Random Forest classification algorithm was used to reprocess the results with Mtry values of 4, 8, and 16. The number of trees (ntree) was randomly set to 250, 500, 750, and 1000. The best results were achieved with a dataset split of 90% training data and 10% test data, using Mtry of 42 and 1000 trees, resulting in 89.97% sensitivity, 92.79% specificity, 80.76% MCC, and 90.42% accuracy. These results demonstrate that the combination of feature extraction and the Random Forest algorithm is effective in classifying lysine proteins.
Comparison of Support Vector Regression and Random Forest Regression Performance in Vehicle Fuel Consumption Prediction Nurdin, Muhaymi; Wamiliana; Junaidi, Akmal; Lumbanraja, Favorisen Rossyking
Integra: Journal of Integrated Mathematics and Computer Science Vol. 1 No. 2 (2024): July
Publisher : Magister Program of Mathematics, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/integrajimcs.20241221

Abstract

Predicting vehicle fuel consumption is an important aspect in improving energy efficiency and supporting sustainable transportation. This study aims to compare the performance of Support Vector Regression (SVR) and Random Forest Regression (RFR) algorithms in predicting combined vehicle fuel consumption (COMBINED, a combination of 55% urban and 45% highway). The Canadian government's Fuel Consumption Ratings dataset was used, with 2015-2023 data (9,185 entries) for training and testing, and 2024 data (764 entries) for further testing. Pre-processing involved StandardScaler for numerical features and OneHotEncoder for categorical features, followed by hyperparameter optimization using Grid Search, resulting in optimal parameters: SVR (C=100, epsilon=0.5, gamma=1) and RFR (n_estimators=200, max_depth=None, min_samples_split=2). Results show RFR is superior with R2 0.8845, RMSE 0.9671, and MAE 0.6566, compared to SVR with R2 0.8648, RMSE 1.0462, and MAE 0.7150. Evaluation on 2024 data and visualization of error distribution corroborate the superiority of RFR. This study concludes RFR is more effective for COMBINED prediction, although SVR is competitive post-optimization, and contributes to the selection of machine learning models for green vehicle technology.
Traffic Violation Modeling Using K-Means Clustering Method: A Case Study in Bandung, Indonesia Junaidi, Akmal; Manurung, Yunita Rosalina; Shofiana, Dewi Asiah; Lumbanraja, Favorisen Rosyking
Integra: Journal of Integrated Mathematics and Computer Science Vol. 1 No. 3 (2024): November
Publisher : Magister Program of Mathematics, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/integrajimcs.20241326

Abstract

Violations of traffic regulations are both an issue and a problem that persists as a feature of life, especially in metropolitan regions such as Bandung. Traffic violation has both behavioral and environmental patterns, with different types of violations occurring at different times during the day. This negligence stems largely from not properly equipping the vehicle with the necessary documents, especially for drivers who do not pay attention to proper document preparation. With the goal of increasing road safety, law enforcement bodies face the ongoing challenge of managing rising traffic violation rates which results in a growing backlog of violation cases and a corresponding backlog workload for police departments. Comprehensive preventive strategies for the problem are extremely difficult to implement in the absence of streamlined mechanisms for the efficient allocation of limited police resources. Currently, agencies responsible for managing violation records are still using a manual desktop system based on Microsoft Excel spreadsheets. This method impedes the analysis of large datasets to derive actionable insights that could inform targeted, data-driven strategies needed to guide proactive measures. In this regard, this study attempts to implement the K-Means clustering technique in order to identify and classify high-incidence traffic violation areas in Bandung. Using this technique, the research classifies the city into three violation risk clusters: very prone, prone, and moderately prone areas. The map of the classes demonstrates the distribution of these clusters spatially, illustrating clearly and vividly how stakeholders can visualise the pattern of traffic violations. This method improves the understanding of data and at the same time boosts purposeful planning for the safety and public traffic order anticipations.
Evaluating User Satisfaction in The Halodoc Application Using a Hybrid CNN-BiLTSM Model for Sentiment Analysis Kurniasari, Dian; Su'admaji, Arif; Lumbanraja, Favorisen Rosyking; Warsono
JURNAL TEKNIK INFORMATIKA Vol. 18 No. 2: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v18i2.42762

Abstract

The growing demand for digital healthcare services in Indonesia has driven the adoption of Online Healthcare Applications (OHApps) such as Halodoc. Despite over 65 million users, maintaining user satisfaction remains a challenge. This study employs sentiment analysis using a hybrid Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) model to classify user review ratings. A dataset of 10,000 Google Play Store reviews was divided into COVID-19 and post-pandemic segments. The methodology includes data collection, pre-processing, and dataset segmentation for training, validation, and testing. Results indicate that the CNN-BiLSTM model surpasses traditional machine learning by combining CNN’s feature extraction with BiLSTM’s long-term dependency capture, achieving 98.71% accuracy on COVID-19 data and 98.16% post-pandemic. Additionally, the model demonstrates strong performance across other key evaluation metrics, with precision, recall, and F1-score. Misclassification analysis highlights minor errors, particularly in ratings 4 and 5. These findings help healthcare providers enhance digital services by identifying user concerns, improving platform features, and optimizing customer engagement. Beyond healthcare, this approach has real-world applications in e-commerce and financial services, where sentiment analysis informs user experience improvements.
Performance evaluation of feature extraction to improve the classification of PTM in C-glycosylation using XGBoost Damayanti, Damayanti; Rosyking Lumbanraja, Favorisen; Junaidi, Akmal; Sutyarso, Sutyarso; Nugroho Susanto, Gregorius; Hendrastuty, Nirwana
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.8466

Abstract

Protein function is regulated by an important mechanism known as post-translational modification (PTM). Covalent and enzymatic protein modifications are added during protein biosynthesis, and such alterations significantly influence the regulation of gene activity and the functionality of proteins. Glycosylation, one type of PTM, involves adding sugar groups to a protein's structure. Numerous illnesses, such as diabetes, cancer, and the flu, have been linked to glycosylation. Therefore, it is critical to predict the presence of glycosylation, whether it occurs or not. Currently, predicting glycosylation sites is still done manually using biological methods, which require repeated experiments and a significant amount of time. To address these challenges, it is essential to rapidly develop computational data models using machine learning methods. In this study, the extreme gradient boosting (XGBoost) method is implemented, and C-glycosylation data is obtained from the publicly accessible UniProt website. The objective is to enhance the accuracy of C-glycosylation prediction using the XGBoost method. Feature extraction is performed using amino acid index (AAindex), composition, transition, and distribution (CTD), solvent AccessiBiLitiEs (SABLE), hydrophobicity, and pseudo amino acid composition (PseAAC) to improve accuracy. The minimum redundancy maximum relevance (MRMR) method is applied for feature selection. The findings of the study demonstrate that the PTM C-glycosylation prediction achieved 100%.
Understanding Consumer Sentiments: A TextBlob-Based Sentiment Analysis Study Kurniasari, Dian; Hdiana, Yazid Zinedine; Lumbanraja, Favorisen R.; Warsono, Warsono; Hadi, Normi Abdul
Integra: Journal of Integrated Mathematics and Computer Science Vol. 2 No. 3 (2025): November
Publisher : Magister Program of Mathematics, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/integrajimcs.20252340

Abstract

This study employs advanced sentiment analysis techniques to enhance the understanding of drug reviews, with a specific focus on TextBlob-based sentiment classification. As the accessibility of health products through pharmacies and online platforms continues to increase, individuals with limited health literacy are increasingly relying on user-generated feedback to inform their decision-making. By utilizing the TextBlob labelling method, this research categorizes user sentiments into positive, neutral, or negative, addressing the limitations inherent in traditional sentiment analysis approaches. The analysis is supported by an innovative model known as BERT, which effectively captures the emotional expression within textual data. The results indicate that the proposed approach consistently achieves an accuracy of 98% across training, validation, and testing phases, highlighting its strong performance in sentiment classification. This accomplishment underscores TextBlob’s ability to consistently and reliably assess user sentiment, thereby enriching the understanding of consumer perspectives in the pharmaceutical industry. The findings highlight the importance of effective sentiment analysis methods in healthcare, offering valuable insights for both consumers and stakeholders. Moreover, this study provides a foundation for future investigations focused on improving sentiment analysis methods across varied datasets, which will enhance the precision and applicability of classification results in different scenarios.
SINERGITAS PENGGIATAN EKONOMI KERAJINAN BATIK LAMPUNG, EKSPLORASI BUDAYA DAN EDUKASI KONSERVASI: ANDANAN BATIK TULIS, NEGERI SAKTI, PESAWARAN, LAMPUNG Rusitati, Elly Lestari; Suroso, Erdi; Warsono, Warsono; Junaidi, Junaidi; Lumbanraja, Favorisen Rosyking; Priyambodo, Priyambodo
Jurnal Pengabdian Kepada Masyarakat Sakai Sambayan Vol. 3 No. 2 (2019)
Publisher : Lembaga Penelitian dan Pengabdian Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jss.v3i2.146

Abstract

Batik Lampung merupakan salah budaya yang khas dengan motifnya dan menjadi salah satu kegiatan ekonomi yang kuat di Provinsi Lampung, termasuk kerajinan Andanan Batik Tulis, yang mencirikan keunikan budaya Lampung. Saat ini selain sebagai kegiatan kebutuhan ekonomi, kerajinan Andanan Batik Tulis sekaligus menjadi media edukasi budaya dan konservasi melalui pengkayaan motif berbasis budaya Lampung, konservasi dan penyediaan media belajar membatik. Penerapan edukasi budaya dan konservasi dilakukan melalui paket eduwisata yang disediakan bagi wisatawan baik lokal maupun asing. Program eduwisata ini mendapat tanggapan positif. Kata Kunci: Andanan Batik Tulis, eduwisata, budaya, konservasi, Lampung
IMPLEMENTASI METODE SUPPORT VECTOR MACHINE DALAM PREDIKSI PERSEBARAN DEMAM BERDARAH DI KOTA BANDAR LAMPUNG Favorisen R Lumbanraja; RM Sulaiman Sani; Didik Kurniawan; Anie Rose Irawati
Jurnal Komputasi Vol. 7 No. 2 (2019)
Publisher : Jurusan Ilmu Komputer Fakultas MIPA Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/komputasi.v7i2.2426

Abstract

Dengue fever is a one of the dangerous diseases and very often causes casualties every year, especially in the tropics or subtropics countries. Dengue fever cases increase during the rainy season, many factors affect the spread of dengue fever, such as vegetation, population and landfills. The aim of this research is to predict the number of cases of Dengue fever using support vector machine. The data used are dengue data in Bandar Lampung City, weather data, population data and distance matrix data between dengue fever events with each other. The amount of data used is 1,080 data with 3 kernels: linear, gaussian and polynomial. In this study four experiments were carried out, the first two experiments were carried out without Feature Selection and the next two experiments were carried out with Feature Selection. After the experiment was found The best performance in the experiment with Feature Selection with 44 Variables. From the experiments conducted, Gaussian kernel achieved the highest R2 value which is 75.52%, while the Linear kernel and Polynomial Kernel achieved R2 value of 74.61% and 75.15%, respectively
Implementasi Metode Ekstraksi Fitur Gabor Filter dan Probablity Neural Network (PNN) untuk Identifikasi Kain Tapis Lampung ADMI SYARIF; AKBAR RISMAWAN TANJUNG; RICO ANDRIAN; FAVORISEN R. LUMBANRAJA
Jurnal Komputasi Vol. 8 No. 2 (2020)
Publisher : Jurusan Ilmu Komputer Fakultas MIPA Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/komputasi.v8i2.2641

Abstract

Tapis Fabric is a traditional clothing of the people of Lampung in the form of a shawl cloth or a sarong made of woven cotton thread with various motifs and ornaments, silver thread or gold thread by embroidered or punched. The pattern of filter cloth is quite complex, unlike the pattern of fabric in general, with its own uniqueness that has become the culture of Lampung society until now. This filter cloth will be investigated by identifying the three types of filter cloth, namely Sasab, Bintang Perak and Gunung Beradu and see the results of its identification. The method used to identify is by combining the Gabor Filter feature extraction method which has frequency and orientation parameters and Probability Neural Network classification methods. Previously, the combination of these two methods was used to identify objects with simple patterns. The results are quite good, such as detecting faces, leaf patterns, and other simple patterns. This research is expected to get maximum identification results on the filter cloth even though it has a pattern that is not simple and will be used as a research report to determine the suitability of the method used for the filter object.
ANALISA KOMPUTASI PARALEL MENGURUTKAN DATA DENGAN METODE RADIX DAN SELECTION Favorisen R. Lumbanraja; Aristoteles Aristoteles; Nadila Rizqi Muttaqina
Jurnal Komputasi Vol. 8 No. 2 (2020)
Publisher : Jurusan Ilmu Komputer Fakultas MIPA Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/komputasi.v8i2.2662

Abstract

Increasing computing power is now achieved by replacing the programming paradigm with parallel programming. Parallel computing is a method of solving problems by dividing the computational load into small parts of the computation sub-process. This study describes the comparative analysis of parallel computations in the Selection Sort and Radix Sort algorithms. The data used are in the form of whole numbers and decimal numbers totaling 100 to 2 million data. The test was carried out with three scenarios, namely using two processors, four processors, and 3 computers connected to each other via a LAN network. The results showed that the parallel Selection Sort algorithm for small data was better than the parallel Radix Sort. On the other hand, parallel Radix Sort is better for millions of data than Selection Sort.
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