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All Journal Proceedings of KNASTIK Bulletin of Electrical Engineering and Informatics Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Jurnal Teknologi Informasi dan Ilmu Komputer Telematika JUITA : Jurnal Informatika Jurnal Ilmiah Dinamika Rekayasa (DINAREK) Jurnal Eksplora Informatika Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal EDUMATIC: Jurnal Pendidikan Informatika Jurnal Abdi Insani Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Jurnal Pendidikan dan Teknologi Indonesia Jurnal Pengabdian Masyarakat Indonesia JUSTIN (Jurnal Sistem dan Teknologi Informasi) Pamasa: Jurnal Pengabdian kepada Masyarakat Journal of Electronics Technology Exploration (JOeTEx) Mengabdi: Jurnal Hasil Kegiatan Bersama Masyarakat Journal of Industrial and Mechanical Engineering Journal of Current Research in Blockchain
Lasmedi Afuan
Informatika, Fakultas Teknik, Universitas Jenderal Soedirman, Indonesia
Agriculture, Biological Sciences & Forestry Humanities 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 Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Public Health Social Sciences Other

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ENHANCING COLLABORATION DATA MANAGEMENT THROUGH DATA WAREHOUSE DESIGN: MEETING BAN-PT ACCREDITATION AND KERMA REPORTING REQUIREMENTS IN HIGHER EDUCATION Wahid, Arif Mu'amar; Afuan, Lasmedi; Utomo, Fandy Setyo
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.6.1747

Abstract

In higher education institutions, effective management of collaboration data is crucial for academic reporting and strategic planning. This study addresses the challenges of managing diverse data types and the necessity for streamlined data management to meet BAN-PT accreditation and Kerma reporting requirements. It aims to design and implement a data warehouse utilizing the star schema for improved accessibility and decision-making. Highlighting the development process, special emphasis is placed on the Extract, Transform, Load (ETL) process with Pentaho to assure data integrity and quality. The methodology involves a systematic approach to constructing the data warehouse, aimed at resolving identified challenges through efficient data organization and quality management. Results demonstrate significant enhancements in data accessibility, reporting efficiency, and quality, leading to reduced administrative efforts and improved decision-making. The research also considers the wider implications of such data management systems in academic administration, suggesting the potential of data warehouses in higher education as benchmarks for similar institutional challenges. Future research directions are recommended for optimizing data warehouse designs and adapting to evolving academic standards, underlining the critical role of advanced data management in meeting stringent accreditation and reporting needs, thus providing a model for technology-driven solutions in educational data management.
ENHANCING SENTIMENT ANALYSIS OF THE 2024 INDONESIAN PRESIDENTIAL INAUGURATION ON X USING SMOTE-OPTIMIZED NAIVE BAYES CLASSIFIER Afuan, Lasmedi; Khanza, Muthia; Zahira Hasyati, Adila
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.1.4290

Abstract

The inauguration of the President and Vice President of Indonesia for the 2024-2029 period has drawn significant public attention, reflecting widespread political and societal interest. This study aims to optimize sentiment analysis of public opinion on X (formerly Twitter) regarding the inauguration by enhancing the Naïve Bayes Classifier (NBC) with the Synthetic Minority Over-sampling Technique (SMOTE). Addressing the issue of class imbalance in sentiment data, the research demonstrates how SMOTE improves classification robustness. The methodology includes data crawling from X, preprocessing involving tokenization, stemming, and TF-IDF feature extraction, and sentiment labeling using TextBlob. Sentiment classification is conducted with NBC, evaluated under conditions with and without SMOTE. Metrics such as accuracy, precision, recall, and F1-score are utilized to assess performance. Results indicate that the application of SMOTE increases the accuracy of NBC from 98% to 99%, with precision improving from 0.98 to 1 and recall maintaining high levels (0.99). This 1% accuracy enhancement underscores the significance of addressing class imbalance for reliable sentiment analysis. The findings contribute to a better understanding of public sentiment during critical political events and highlight the effectiveness of SMOTE in improving text classification tasks. This research provides valuable insights into leveraging machine learning techniques for analyzing imbalanced datasets, offering implications for both academic and practical applications in sentiment analysis and political studies.
IMPLEMENTATION OF TEXT MINING ON SONG LYRICS FOR SONG CLASSIFICATION BASED ON EMOTION USING WEBSITE-BASED LOGISTIC REGRESSION Rahayu, Swahesti Puspita; Afuan, Lasmedi; Yunindar, Galih Arditiya
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.1.4429

Abstract

Music has become an essential medium for expressing emotions and enriching human social experiences. However, the manual interpretation of emotions in song lyrics is often inaccurate and time-consuming, especially for complex or ambiguous lyrics. This creates a need for an automated system that can improve the accuracy and efficiency of emotion classification in song lyrics. Various algorithms, such as K-Nearest Neighbor (K-NN), Naive Bayes Classifier, and Support Vector Machine (SVM), have been applied for emotion classification in song lyrics. Previous research has shown that SVM combined with Particle Swarm Optimization (PSO) achieves an accuracy of up to 90%, while K-NN with feature selection produces the highest f-measure of 66.93%, and Naive Bayes achieves an accuracy of up to 45%. In this study, the Logistic Regression algorithm, supported by the Term Frequency-Inverse Document Frequency (TF-IDF) method, is applied to enhance the accuracy of emotion classification. Evaluation results indicate that the model with figurative language transformation achieves a higher accuracy (93.52%) compared to the model without figurative language transformation (92.31%), demonstrating that figurative language contributes to the richness of emotional expression recognized by the model. This model shows competitive results and can be compared to SVM using PSO while providing better performance than K-NN and Naive Bayes. The system implementation is web-based using the Streamlit framework, allowing users to input lyrics and obtain interactive emotion predictions. This research contributes to the analysis of music emotions and offers an efficient and more accessible alternative for emotion classification in song lyrics.
Penguatan Manajemen pada Usaha Kerajinan Bambu Jihan Craft Desa Somakaton Najmudin Najmudin; Sri Lestari; Lasmedi Afuan; Devani Laksmi Indyastuti
MENGABDI : Jurnal Hasil Kegiatan Bersama Masyarakat Vol. 2 No. 4 (2024): Agustus: MENGABDI : Jurnal Hasil Kegiatan Bersama Masyarakat
Publisher : Asosiasi Riset Ekonomi dan Akuntansi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/mengabdi.v2i4.798

Abstract

Partners in this program are bamboo craftsmen Jihan Craft, Somakaton Village, Somagede District, Banyumas Regency. The problems faced by partners are lack of equipment in running their business; very limited capabilities in business management consisting of production management, HR, marketing and finance; limited ability in bookkeeping and financial reporting; limited ability to carry out online marketing activities; do not form groups; do not understand group management; and do not have the skills to carry out group administration. To achieve the goals, activities are carried out in the form of counseling, practice and mentoring. The evaluation method is carried out by comparing the level of knowledge and abilities as well as the production and marketing performance of partners before and after the activity. The benefits of this service activity are that partners get additional mechanical production equipment so they can increase the quantity and quality of their products and can produce more efficiently, partners' business management abilities increase, they have the ability to do bookkeeping and financial reporting, they can carry out online marketing activities well, the formation of groups Jihan Craft business officially, understand and can practice group management well; and can carry out group administration activities well.
A Comprehensive Benchmarking Pipeline for Transformer-Based Sentiment Analysis using Cross-Validated Metrics Abidin, Dodo Zaenal; Afuan, Lasmedi; Toscany, Afrizal Nehemia; Nurhadi, Nurhadi
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.4894

Abstract

Transformer-based models have significantly advanced sentiment analysis in natural language processing. However, many existing studies still lack robust, cross-validated evaluations and comprehensive performance reporting. This study proposes an integrated benchmarking pipeline for sentiment classification on the IMDb dataset using BERT, RoBERTa, and DistilBERT. The methodology includes systematic preprocessing, stratified 5-fold cross-validation, and aggregate evaluation through confusion matrices, ROC and precision-recall (PR) curves, and multi-metric classification reports. Experimental results demonstrate that all models achieve high accuracy, precision, recall, and F1-score, with RoBERTa leading overall (94.1% mean accuracy and F1), followed by BERT (92.8%) and DistilBERT (92.1%). All models exceed 0.97 in ROC-AUC and PR-AUC, confirming strong discriminative capability. Compared to prior approaches, this pipeline enhances result robustness, interpretability, and reproducibility. The provided results and open-source code offer a reliable reference for future research and practical deployment. This study is limited to the IMDb dataset in English, suggesting future work on multilingual, cross-domain, and explainable AI integration.
Comparative performance analysis of LSTM, GRU, and bidirectional neural networks for political ideology classification Afuan, Lasmedi; Hidayat, Nurul; Permadi, Ipung; Iqbal, Iqbal; Suprihanto, Didit; Bintang Pradana Yosua, Panky; Alfarez Marchelian, Reyno
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9980

Abstract

Political ideology classification is crucial for understanding social polarization, monitoring democratic processes, and identifying bias on online platforms. This study compares the performance of long short-term memory (LSTM), gated recurrent unit (GRU), and bidirectional GRU (Bi-GRU) neural network models in classifying liberal and conservative political ideologies from social media text data. The Bi-GRU achieved the best results with 88.75% accuracy and 89.16% F1-score, highlighting its strength in contextual analysis. These findings suggest their applicability in areas such as election monitoring and the analysis of political discourse. This study contributes to the field of political text classification by offering a comparative analysis of deep learning architectures. The dataset utilized covers a wide range of issues, including social, political, economic, religious, and racial topics, demonstrating its comprehensive nature. Visualizations using WordCloud and uniform manifold approximation and projection (UMAP) reveal distinct ideological patterns, validating the dataset’s quality for training models. The findings underscore the importance of utilizing advanced bidirectional architectures for nuanced tasks, such as ideology classification, where contextual understanding is crucial. These insights open avenues for future research, such as the application of Bi-GRU in analyzing multilingual political ideologies or real-time sentiment tracking during election campaigns.
Pemilihan Lokasi Terbaik Pemasangan Billboard Untuk Media Promosi Program Studi Menggunakan Metode ELECTRE II Aji, Pandu Wahyu; Muhammad, Katon; Alkaf, Zakiyyan; Afuan, Lasmedi; Hidayat, Nurul
Journal of Industrial and Mechanical Engineering Vol 3 No 1 (2025): Journal of Industrial and Mechanical Engineering
Publisher : Department of Industrial Engineering, Universitas Jenderal Soedirman.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jimien.2025.3.1.14600

Abstract

Dalam upaya meningkatkan visibilitas dan daya tarik Program Studi Teknik Industri Unsoed, pemilihan lokasi pemasangan billboard menjadi aspek penting dalam strategi promosi. Lokasi yang strategis perlu dianalisas secara objektif dengan mempertimbangkan beberapa kriteria, seperti biaya, ukuran billboard, kepadatan lalu lintas, durasi lampu lalu lintas, dan jarak ke pusat kota. Pemilihan ini bertujuan menentukan lokasi terbaik untuk pemasangan billboard promosi di wilayah Purwokerto menggunakan metode Electre II. Metode ini melibatkan normaliasai data, pembobotan kriteria, serta analisis nilai concordance dan discordance untuk memperoleh peringkat alternatif lokasi. Hasil analisis menunjukan bahwa Simpang 4 Srimaya merupakan lokasi paling optimal dengan skor tertinggi, ditunjang kepadatan lalu lintas tinggi (150 kendaraan per menit) dan rata-rata durasi lampu lalu lintas yang lama (125 detik). Lokasi potensial lainnya adalah simpanng 4 Klenteng SKJ dan Simpang 4 GOR Satria. Temuan ini memberikan rekomendasi strategis dalam pemilihan lokasi iklan luar ruang yang efektif dan eisien untuk promosi program studi kepada calon mahasiswa.
RNN-Based Intrusion Detection System for Internet of Vehicles with IG, PCA, and RF Feature Selection Purnama, Benni; Winanto, Eko Arip; Sharipuddin, Sharipuddin; Sandra, Dodi; Nurhadi, Nurhadi; Afuan, Lasmedi
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5293

Abstract

Cyberattacks in the Internet of Vehicles (IoV) threaten road safety and data integrity, requiring intrusion detection systems (IDS) that capture temporal patterns in vehicular traffic. This study develops a Recurrent Neural Network (RNN)-based IDS and evaluates three feature-selection strategies—Information Gain (IG), Principal Component Analysis (PCA), and Random Forest (RF)—on the CICIoV2024 dataset. Features are normalized using Min–Max scaling before being fed into the RNN classifier. The models achieve perfect classification on held-out tests (accuracy/precision/recall/F1 = 1.00). However, probabilistic evaluation reveals low ROC–AUC scores (IG: 0.572, PCA: 0.429, RF: 0.415), indicating limited discriminative margins and potential overfitting or calibration issues despite flawless confusion matrices. PCA and RF further reduce computational overhead during inference compared to IG. These findings highlight that relying solely on accuracy can be misleading for IDS evaluation; temporal RNNs should be complemented with probability-aware training, calibration, or hybrid architectures. This work contributes a temporal-aware IDS framework for IoV and motivates future research on real-time deployment, hybrid RNN-CNN/LSTM models, and adversarial robustness to improve generalization and safety of connected vehicles
Aplikasi untuk Mengenerate dan Pengiriman Sertifikat Webinar di Masa Pandemi Corona Virus Disease 19 Afuan, Lasmedi; Hidayat, Nurul; Nurhayati, Siti
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8 No 4: Agustus 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2021844984

Abstract

Pandemi Coronavirus Disease (COVID-19) terjadi di lebih dari 200 negara termasuk Indonesia, telah mengubah kebiasaan masyarakat dalam berkomunikasi dan berinteraksi. Covid-19 memaksa masyarakat untuk berkomunikasi dan berinteraksi secara online dengan menggunakan platform seperti Zoom, Google-meet, Webex, dan platform lainnya. Salah satu sektor yang mengalami dampak yang mengharuskan berkomunikasi dan berinteraksi secara online  adalah sektor pendidikan, hal tersebut bertujuan untuk mencegah penyebaran dan penularan Covid-19. Salah satu kegiatan di sektor pendidikan yang harus dilaksanakan secara online adalah seminar. Seminar yang dilaksanakan secara online di masa pandemi  Covid-19 ini dikenal dengan sebutan Webinar. Pada webinar, pengelola webinar menghadapi permasalahan setelah pelaksanaan webinar yaitu bagaimana mengenerate dan mengirimkan sertifikat kepada peserta webinar secara massal. Penelitian ini telah mengembangkan aplikasi berbasis web yang diberi nama Cobinar. Cobinar merupakan aplikasi yang digunakan untuk membantu pengelola webinar dalam mengatasi permasalahan yang berkaitan generate dan pengiriman sertifikat massal kepada peserta webinar. Aplikasi Cobinar dikembangkan dengan menggunakan bahasa pemrograman PHP dan menggunakan Database Management System (DBMS) MySql. Sedangkan metode pengembangan perangkat lunak yang digunakan adalah Waterfall. Berdasarkan hasil uji coba, aplikasi Cobinar ini mampu mengenerate dan mengirimkan sertifikat ke peserta webinar dengan lebih efektif dan efisien, hal ini dapat dilihat dari hasil pengujian menggunakan  User Acceptance Test (UAT)  yang menyatakan bahwa 95 persen pengguna menyatakan bahwa aplikasi Cobinar mudah untuk dijalankan. Kontribusi utama dari penelitian ini adalah pengembangan sebuah aplikasi yang dapat digunakan oleh pengelola webinar untuk mengenerate dan mengirimkan sertifikat webinar kepada peserta. AbstractThe Coronavirus Disease (COVID-19) pandemic occurred in more than 200 countries, including Indonesia, which has changed people's habits in communicating and interacting. Covid-19 forces people to communicate and interact online using Zoom, Google-meet, Webex, and other platforms. One sector that has experienced the impact of communicating and interacting online is the education sector, which aims to prevent the spread and transmission of Covid-19. One of the activities in the education sector that must be carried out online is a conference. This conference, which was held online during the Covid-19 pandemic, is known as a Webinar. The webinar manager faced a problem after the webinar, namely, how to generate and send certificates to webinar participants. This research has developed a web-based application called Cobinar. Cobinar is an application used to assist webinar managers in overcoming problems related to generating and sending mass certificates to webinar participants. The Cobinar application was developed using the PHP programming language and using the MySql Database Management System (DBMS). While the software development method used is Waterfall. The Cobinar application can generate and send certificates to webinar participants more effectively and efficiently based on testing. It can be seen from the results of testing using the User Acceptance Test (UAT), which states that 95 percent of users state that the Cobinar application is easy to operate. The main contribution of this research is the development of an application that webinar administrators can use to generate and send webinar certificates to participants.
Analisis Sentimen Kemungkinan Depresi dan Kecemasan pada Twitter Menggunakan Support Vector Machine Darmawan, Ferry; Joe, Michael; Kurniawan, Yogiek Indra; Afuan, Lasmedi
Eksplora Informatika Vol 13 No 1 (2023): Jurnal Eksplora Informatika
Publisher : Institut Teknologi dan Bisnis STIKOM Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30864/eksplora.v13i1.854

Abstract

Co-Authors Abidin, Dodo Zaenal Adi Pangestu Adyatma, Adrian Dwinanda Afrizal Nehemia Toscany Ahmad Ashari Ahmad Fauzi Ridlwan Aji, Pandu Wahyu Alfarez Marchelian, Reyno Alkaf, Zakiyyan Andreas, Roy Anin Ammbya Soulani Arief Kelik Arief Kelik Nugroho Arief Kelik Nugroho Arief Kelik Nugroho Arief Kelik Nugroho Arkan, Naofal Dhia As'ad, Mohamad Faris Asmoro Widagdo, Asmoro Bangun Wijayanto Bintang Pradana Yosua, Panky Dadang Iskandar Dadang Iskandar Daffa Ammar Muaafii Daffa Naufaldi Al Rasyid Didit Suprihanto, Didit Dodi Sandra Eddy Maryanto Eddy Maryanto Fandy Setyo Utomo Faris Akbar Abimanyu Febri Sutomo Ferry Darmawan Hidayat, Nurul Indah Cahya Febriani Indyastuti, Devani Laksmi Ipung Permadi Ipung Permadi Ipung Permadi Ipung Permadi Iqbal Iqbal Irfan Agus Tiawan Jasmir, Jasmir Joe, Michael Khanza, Muthia Kharisun, Kharisun Kurniawan, Yogiek Indra Maria Ulfa Chasanah Muhammad Fikri Rivaldi Muhammad Luthfi Muhammad Randy Cahya Mardika Muhammad Zein Albalki Muhammad, Katon Mulki Indana Zulfa, Mulki Indana Musaadah, Khalimah Najmudin Nandha Arwiansyah Nasichatul Umayah Niko Siameva Uletika Nofiyati Nofiyati, Nofiyati Nofiyati, Nofiyati Nur Chasanah Nurhadi Nurul Hidayat Nurul Hidayat Nurul Ismailiah Priandika Ratmadani Anugrah Purnama, Benni R. Rizal Isnanto Rahayu, Swahesti Puspita Rif’an, Muhammad Rista Afifah Rochmat Mulyo Sugihono Said, Rahaini Mohd Sari, Enjelita Sharipuddin, Sharipuddin Siti Nurhayati Slamet Widodo SRI LESTARI Susi Setianingsih Teguh Cahyono Tuti Alawiyah Victoria Angela Sugianto Wahid, Arif Mu'amar Yohanes Suyanto Yunindar, Galih Arditiya Zahira Hasyati, Adila