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All Journal Jurnal Teknik Elektro InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JPEK (Jurnal Pendidikan Ekonomi dan Kewirausahaan) JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal SASAK : Desain Visual dan Komunikasi Jurnal Bumigora Information Technology (BITe) Innovation in Research of Informatics (INNOVATICS) Informatics and Digital Expert (INDEX) Jurnal Aplikasi Teknologi Informasi dan Manajemen (JATIM) ADMA: Jurnal Pengabdian dan Pemberdayaan Masyarakat Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) Bakti Sekawan : Jurnal Pengabdian Masyarakat Jurnal Ilmiah Pengabdian dan Inovasi Panthera : Jurnal Ilmiah Pendidikan Sains dan Terapan Valid Jurnal Pengabdian Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK)
Muhammad Innuddin
Universitas Bumigora
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology 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 Immunology & microbiology Languange, Linguistic, Communication & Media Mathematics Nursing Physics Social Sciences Other

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Optimalisasi Model Bahasa dan Sistem Ekonomi Berbasis Teks dengan Proximal Policy Optimization: Studi Kasus dalam NLP Modern Darmawan, Irwan; Ramadhani, Nilam; Nazir Arifin, Mohammad; -, Ubaidi; Puspa Dewi, Nindian; Innuddin, Muhammad
Jurnal Bumigora Information Technology (BITe) Vol. 7 No. 1 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/bite.v7i1.5222

Abstract

Background: This study investigates the use of the Proximal Policy Optimization (PPO) algorithm in two text-based case studies: alignment of large language models (LLMs) with human preferences and dynamic pricing based on customer reviews. In the LLM case, PPO combined with preference-based learning significantly improves alignment, BLEU, and human-likeness scores.Objective: This research aims to evaluate PPO’s effectiveness in text-based decision-making through these two cases.Methods: The method employed is reinforcement learning experimentation using the PPO approach. For the LLM case, PPO is integrated with preference learning to enhance alignment, BLEU, and human-like output. Meanwhile, in the economic scenario, PPO produces adaptive pricing strategies with high accuracy or low Mean Absolute Error (MAE) and the best cumulative rewards, outperforming the A3C and DDPG algorithms. Cross-validation and ablation studies assessed PPO’s generalization capability and the contribution of reward components, clipping, and exploration strategies.Result: The findings demonstrate that PPO excels across distinct domains and offers a stable and efficient solution for text-based tasks.Conclusion: The findings confirm its flexibility for various NLP applications and intelligent decision-making systems 
Optimizing Sentiment Analysis for Lombok Tourism Using SMOTE and Chi-Square with Machine Learning Hairani; Anggrawan, Anthony; Muhammad Ridho Akbar; Khasnur Hidjah; Muhammad Innuddin
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6623

Abstract

Tourism is a vital economic sector for Lombok Island, which is renowned for its natural beauty and cultural richness as a top destination. The rapid growth of tourism in Lombok requires a deep understanding of tourists' perceptions and sentiments to ensure an optimal service quality. The sentiment analysis of online reviews is valuable for identifying service strengths and weaknesses and addressing tourists' needs more effectively. This not only enhances tourist satisfaction, but also aids in the design of more effective marketing strategies. However, text data analysis from online reviews presents unique challenges such as noise, class imbalance, and numerous features that may affect classification results. Therefore, this study aims to classify tourist sentiment toward Lombok tourism using machine learning methods combined with feature selection and oversampling techniques. This study focuses on optimizing sentiment analysis of tourism-related tweets using a combination of SMOTE oversampling and Chi-Square feature selection on improving classification performance without hyperparameter tuning. The study applies machine learning methods, such as SVM and Naïve Bayes, with feature selection and oversampling using Chi-Square and SMOTE. The dataset used was sentiment data regarding Lombok tourism obtained from Twitter in 2023, consisting of 940 instances divided into three classes: Negative, Neutral, and Positive. The research findings show that the use of SMOTE and Chi-Square can improve the accuracy of the SVM and Naive Bayes methods. Without optimization, the SVM method achieved an accuracy of 73.93% and a Naive Bayes of 67.02%. After optimization with SMOTE and Chi-Square, the accuracy increased for SVM by 90% and Naive Bayes by 84% to classify tourist sentiment towards Lombok tourism. The implications indicate that combining data balancing using SMOTE with feature selection via Chi-Square effectively improves the performance of sentiment classification models for tourist opinions on Lombok's tourism.
Sosialisasi Peran Penggunaan Management Learning System sebagai Platform Pembelajaran Daring untuk Mendukung Pembelajaran Mandiri Aprianto, Dedi; Mardedi, Lalu Zazuli Azhar; Sutarman, Sutarman; Hendri, Wira; Hairani, Hairani; Innuddin, Muhammad; Rahmawati, Lela
ADMA : Jurnal Pengabdian dan Pemberdayaan Masyarakat Vol. 5 No. 2 (2025): ADMA: Jurnal Pengabdian dan Pemberdayaan Masyarakat
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/adma.v5i2.4569

Abstract

keterbatasan kesadaran mahasiswa tentang optimalisasi penggunaan MLS sebagai media yang dapat menunjang belajar mandiri meskipun telah diterapkan sebagai media belajar komplementer, tetapi belum sepenuhnya efektif. Sehingga hal ini mengurangi potensi maksimal dalam mendukung proses belajar mereka. Kegiatan Pengabdian kepada Masyarakat (PkM) ini bertujuan untuk meningkatkan pemahaman dan kesadaran mahasiswa mengenai penggunaan Management Learning System (MLS) dalam mendukung pembelajaran mandiri. Isu yang diangkat adalah rendahnya kesadaran mahasiswa tentang MLS, yang berdampak pada efektivitas pembelajaran daring guna meningkatkan pembelajaran mandiri. Untuk mencapai tujuan tersebut, metode yang digunakan, yaitu sosialisasi; ceramah, diskusi, tanya-jawab, dan demonstrasi langsung. Kegiatan ini terdiri dari tiga tahapan: persiapan, pelaksanaan, dan evaluasi. Hasil kegiatan PkM ini menunjukkan bahwa bahwa (85%) peserta merasa lebih memahami cara menggunakan MLS setelah pelatihan, dan (78%) merasa siap untuk menerapkan MLS dalam pembelajaran mandiri. Selain itu, (90%) peserta menyatakan bahwa kegiatan ini bermanfaat bagi mereka. Kesimpulan kegiatann ini menunjukkan keberhasilan dalam memperkuat pemahaman mahasiswa tentang penggunaan MLS, sekaligus mendukung peningkatan pembelajaran mandiri dalam konteks pembelajaran daring.
Improving Classification Performance on Imbalanced Stroke Datasets Using Oversampling Techniques Innuddin, Muhammad; Hairani; Jauhari, M. Thonthowi; Mardedi, Lalu Zazuli Azhar
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 5 (2025): October 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i5.6859

Abstract

Stroke is the second leading cause of death worldwide and a major factor in long-term disability. Although early detection based on machine learning continues to be developed, it still faces challenges in the form of data imbalance, which can reduce classification performance. This study aimed to evaluate the effectiveness of several oversampling techniques, such as SMOTE, Borderline-SMOTE, and SVM-SMOTE, in improving the performance of stroke classification models on imbalanced data. The methods used included the application of three oversampling techniques, namely SMOTE, Borderline-SMOTE, and SVM-SMOTE, to balance the data distribution. Furthermore, Random Forest and XGBoost algorithms were used as classification models to identify stroke cases. The results of this study show that the use of oversampling techniques significantly improves model performance, especially in MCC and AUC metrics, compared to models without oversampling. Borderline-SMOTE provides the best results, with the highest accuracy of 96.45% on Random Forest and 96.41% on XGBoost, as well as MCC and AUC values that are consistently superior to other techniques. Furthermore, this study highlights that the use of Borderline-SMOTE significantly enhances model performance, as evidenced by an increase in MCC of 87.51% and an AUC of 45.40% in Random Forest, along with an increase in MCC of 76.52% and an AUC of 41.81% in XGBoost. These findings confirm that Borderline-SMOTE is an effective approach for dealing with data imbalance and improving the model's ability to detect minority classes in stroke classification.
Sosialisasi Pengenalan Dasar Teknologi Jaringan sebagai Pengetahuan Dasar dalam Bisnis Wifi Muhammad Innuddin; Andi Sofyan Anas; Dedi Febry Rachman; Suryati; Phyta Rahima
Jurnal Ilmiah Pengabdian dan Inovasi Vol. 2 No. 4 (2024): Jurnal Ilmiah Pengabdian dan Inovasi (Juni)
Publisher : Insan Kreasi Media

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57248/jilpi.v2i4.387

Abstract

This socialization or training packaged as a Community Service Activity is motivated by the community's lack of understanding regarding the WiFi network when it wants to be developed into a business by the community itself. Based on the results of the service team's evaluation using pre-tests, it was found that people still did not understand what was needed and the function of each device, so for this problem the service team offered a solution in the form of training on WiFi networks. In this training the service team divided it into two sessions, namely a theory session and a field practice session. In this service process, the service team uses the planning, do, check and act (PDCA) method. This method has 4 stages, namely planning, do, check and act. The implementation process went smoothly according to plan and obtained satisfactory results. The evaluation results prove this by providing a post-test after the training and these results show that the public's understanding of WiFi networks has improved for the better.  
Pelatihan Technopreneurship di Era Digital: Strategi Pemberdayaan Wirausaha Lokal Menuju Transformasi Digital Berkelanjutan Rachman, Dedy Febry; Amri, Syaiful; Innuddin, Muhammad
Jurnal Ilmiah Pengabdian dan Inovasi Vol. 3 No. 2 (2024): Jurnal Ilmiah Pengabdian dan Inovasi (Desember)
Publisher : Insan Kreasi Media

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57248/jilpi.v3i2.541

Abstract

Pelatihan technopreneurship di era digital merupakan upaya strategis untuk meningkatkan kapasitas wirausaha lokal dalam memanfaatkan teknologi guna mendukung pengembangan usaha. Pelatihan ini dirancang untuk memberikan pemahaman dan keterampilan praktis kepada wirausaha lokal agar mampu mengoptimalkan teknologi digital, seperti pemasaran online, e-commerce, manajemen usaha berbasis aplikasi, dan inovasi produk di desa Rempung Kecamatan Pringgasela Lombok Timur. Metode penelitian yang digunakan adalah Participatory Action Research (PAR), yang melibatkan wirausaha sebagai mitra aktif dalam seluruh tahapan, mulai dari identifikasi masalah, perencanaan, implementasi, hingga evaluasi dan refleksi. Pelatihan ini dilakukan dengan pendekatan blended learning, mengombinasikan sesi tatap muka, pembelajaran daring, simulasi bisnis, serta pendampingan oleh mentor berpengalaman. Hasil yang diharapkan dari pelatihan ini meliputi peningkatan keterampilan digital, pertumbuhan bisnis melalui platform online, dan kemampuan wirausaha dalam menciptakan inovasi berbasis teknologi. Selain itu, pelatihan ini bertujuan membangun jejaring komunitas wirausaha digital yang mendukung kolaborasi dan berbagi pengetahuan di tingkat lokal. Program ini dirancang untuk membantu wirausaha lokal menghadapi tantangan transformasi digital, memperluas pasar, meningkatkan produktivitas, dan memperkuat daya saing di era global. Dengan pendekatan partisipatif dan berorientasi pada tindakan, pelatihan technopreneurship di era digital diharapkan menjadi katalisator bagi pemberdayaan ekonomi lokal yang berkelanjutan pada tahun 2024.
Web-Based Application for Toddler Nutrition Classification Using C4.5 Algorithm Hairani, Hairani; Nurhayati, Lilik; Innuddin, Muhammad
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 1 No. 2 (2022): September 2022
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v1i2.2387

Abstract

Health is something that is important for everyone, from year to year various efforts have been developed to get better and quality health. Good nutritional status for toddlers will contribute to their health and also the growth and development of toddlers. Fulfillment of nutrition in children under five years old (toddlers) is a factor that needs to be considered in maintaining health, because toddlerhood is a period of development that is vulnerable to nutritional problems. There are more than 100 toddler data registered at the Integrated Healthcare Center in Peresak Village, Narmada District, West Lombok Regency. The book contains data on toddlers along with the results of weighing which is carried out every month. However, to classify the nutritional status of toddlers, they are still going through the process of recording in a notebook by recording the measurement results and then looking at the reference table to determine their nutritional status. This method is still conventional or manual so it takes a long time to determine the nutritional status. Therefore, the solution in this study is to develop a web-based application for the classification of the nutritional status of children under five using the C4.5 method. The stages of this research consisted of problem analysis, collection of 197 instances of nutritional status datasets obtained from Integrated Healthcare Center Presak, analysis of system requirements, use case design, implementation using the C4.5 method, and performance testing based on accuracy, sensitivity, and specificity. The results of this study are a website-based application for the classification of the nutritional status of children under five using the C4.5 method. The performance of the C4.5 method in the classification of the nutritional status of toddlers using testing data as much as 20% gets an accuracy of 95%, sensitivity of 100%, and specificity of 66.6%. Thus, the C4.5 method can be used to classify the nutritional status of children under five, because it has a very good performance.
Electric Vehicle Sales-Prediction Application Using Backpropagation Algorithm Based on Web Ramadhanti, Ramadhanti; Hairani, Hairani; Innuddin, Muhammad
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 2 No. 2 (2023): September 2023
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v2i2.3388

Abstract

The accuracy of predicting future product sales is needed to minimize losses and gain profits. Inventory of goods carried out manually or improper product inventory planning causes the number of goods to accumulate due to the small number of requests, so the goods are damaged. Therefore, a sales prediction system with high accuracy is needed to assist in stocking electric vehicles. This research aimed to predict electric vehicle sales using the web-based backpropagation method. This study uses the backpropagation method to predict electric vehicle sales data from 2015 to 2022. The data is divided into 84 instances as training data and 12 instances as testing data. The result of this study was that the backpropagation method obtained a MAPE error rate of 6.25%. Thus, the backpropagation method can be used for predicting electric vehicle sales because it has a very accurate performance level.
Analisa Penerapan Fitur Firewall pada Mikrotik untuk Mengamankan dari Serangan Denial of Service (DoS) Wijaya, Frederico Indra; Innuddin, Muhammad; Latif, Kurniadin Abd.
Panthera : Jurnal Ilmiah Pendidikan Sains dan Terapan Vol. 5 No. 3 (2025): July
Publisher : Lembaga Pendidikan, Penelitian, dan Pengabdian Kamandanu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36312/panthera.v5i3.546

Abstract

Cybercrimes such as Denial of Service (DoS) attacks pose a serious threat to the stability of computer networks, especially those without adequate protection. This study aims to analyze the implementation of firewall features on Mikrotik routers as an effort to improve network security against DoS attacks. The method used refers to the Network Development Life Cycle (NDLC) approach, which includes the stages of analysis, design, simulation, implementation, and testing. The results show that the firewall configuration successfully closed several vulnerable ports (FTP, SSH, Telnet, and HTTP) while maintaining critical operational ports (DNS and Winbox). Furthermore, the CPU load on the router decreased dramatically from 100% to 3-4% after implementation, indicating success in mitigating malicious traffic. These findings confirm that the firewall feature on Mikrotik can significantly increase network resilience against DoS attacks.
Co-Authors -, Ubaidi Abdillah, Mokhammad Nurkholis Admojo, Fadhila Tangguh Ahmad Fathoni Ahmad Zuli Amrullah Andi Sofyan Anas Anthony Anggrawan Dedi Aprianto Dedi Febry Rachman Dedy Febry Rachman Dedy Febry Rahman Dyah Susilowati Eka Sudarsana, I Putu Fauzan Natsir Gibran Satya Nugraha Hairani Hairani Hammad, Rifqi Hasbullah Hasbullah Hasbullah Hasbullah Hersanius Kurnia Peong Ida Putu Andika Irwan Darmawan Jauhari, M. Thonthowi Khasnur Hidjah Kurniadin Abd Latif Kurniawan Kurniawan Lilik Nurhayati Mardedi, Lalu Zazuli Azhar Mardedi, Lalu Zazuli Azhar Mualfah, Desti Muhammad Arfa Muhammad Ridho Akbar Muhammad Sahdan Muhammad Zulkarnaen Haris Nazir Arifin, Mohammad Nurhayati, Lilik Ondi Asroni Pahrul Irfan Phyta Rahima Pio Pratama, I Wayan Puspa Dewi, Nindian Rahmawati, Lela Ramadhani, Nilam Ramadhanti Ramadhanti Ramadhanti, Ramadhanti Ria Rismayati Rifqi Hammad Samsul Hadi Slamet Risnanto Sri Anita Rachman Suratno, Danang Mulyadipa Suryati Sutarman Sutarman Syaiful Amri Wijaya, Frederico Indra Windiawati, Ai Wulan Wira Hendri Zilullah Nazir Hadi