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All Journal Jurnal Informatika JURNAL SISTEM INFORMASI BISNIS TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Sarjana Teknik Informatika JUITA : Jurnal Informatika Jurnal Aplikasi Bisnis dan Manajemen (JABM) E-Journal Jurnal Teknologi dan Sistem Komputer JIEET (Journal of Information Engineering and Educational Technology) Indonesian Journal of Information System BAREKENG: Jurnal Ilmu Matematika dan Terapan JITK (Jurnal Ilmu Pengetahuan dan Komputer) JMM (Jurnal Masyarakat Mandiri) SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI ILKOM Jurnal Ilmiah MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal KOMPUTA : Jurnal Ilmiah Komputer dan Informatika GERVASI: Jurnal Pengabdian kepada Masyarakat INSIST (International Series on Interdisciplinary Research) Jurnal Informatika Global Jurnal Teknologi Terpadu bit-Tech Jurnal Abdimas Mandiri Indonesian Journal of Electrical Engineering and Computer Science Reswara: Jurnal Pengabdian Kepada Masyarakat Journal of Computer Networks, Architecture and High Performance Computing Idealis : Indonesia Journal Information System Lumbung Inovasi: Jurnal Pengabdian Kepada Masyarakat Indonesian Community Journal Jurnal Teknologi Sistem Informasi Jurnal Ilmiah Teknik Informatika dan Komunikasi Jurnal INFOTEL SISFOTENIKA Jurnal Teknik Informatika dan Teknologi Informasi
Rendra Gustriansyah
Universitas Indo Global Mandiri, Indonesia
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Other

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A SARIMA APPROACH WITH PARAMETER OPTIMIZATION FOR ENHANCING FORECAST ACCURACY FOR NATIVE CHICKEN EGG PRODUCTION Gustriansyah, Rendra; Dewi, Deshinta Arrova; Puspasari, Shinta; Sanmorino, Ahmad
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp1331-1344

Abstract

This study aims to accurately forecast monthly native chicken egg production using the Seasonal Autoregressive Integrated Moving Average (SARIMA) model with parameter optimization. The optimization process was conducted through a combination of auto.arima() initialization and an exhaustive grid search across the parameter space, evaluated using multiple performance metrics. The dataset comprised monthly production data from Magelang City, Indonesia, spanning the period from 2016 to 2022. The best-performing model, SARIMA (2,1,2)(1,0,1,12), achieved an R² of 0.89, MAE of 82.13, RMSE of 92.92, MAPE of 7.21%, and MASE of 0.67 on the testing set, indicating satisfactory forecasting performance. Compared with the non-optimized SARIMA baseline, the optimized model showed improved predictive accuracy. However, the residuals did not follow a normal distribution, suggesting potential limitations in model assumptions. Moreover, the study is limited by its focus on a single geographic location and native chicken production data, which may restrict its generalizability. Despite these limitations, the findings demonstrate that parameter optimization in SARIMA enhances forecast accuracy and can support better planning for food security initiatives.
Analysis Of Public Sentiment Towards The Free Nutritious Meal Program In Schools Based On Tweets Using The K-Nearest Neighbors Method Maharani, Aiga Rizki; Gustriansyah, Rendra; irfani, muhammad haviz
JATISI Vol 12 No 4 (2025): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v12i4.13446

Abstract

The public sentiment analysis of the free nutritious meal program in schools was conducted based on data from the social media platform Twitter (X). This program is an initiative by the Indonesian government aimed at improving the nutritional quality of children, particularly those from underprivileged families, as well as reducing stunting rates. The data used consisted of 3,007 tweets that had undergone preprocessing, manual labeling, and class balancing using oversampling techniques. The K-Nearest Neighbors (K-NN) method was applied to classify sentiment into three categories: positive, negative, and neutral. The data was split with 80% used for training and 20% for testing. The analysis process included data representation using TF-IDF and model evaluation using metrics such as accuracy, precision, recall, and F1-score. Evaluation results showed that the K-NN model with K=3 achieved an accuracy of 82%, with the best performance in classifying negative sentiment tweets (recall = 1.00, F1-score = 0.93). These findings indicate that public opinion toward the program tends to be negative, mainly due to concerns over budget allocation and food distribution. This study is expected to provide input for the government in designing more effective and responsive communication strategies and public policies.
ENHANCING ELEMENTARY STUDENT’S KNOWLEDGE THROUGH WEB SECURITY FUNDAMENTALS COUNSELING Sanmorino, Ahmad; Gustriansyah, Rendra; Puspasari, Shinta
JMM (Jurnal Masyarakat Mandiri) Vol 8, No 2 (2024): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jmm.v8i2.21724

Abstract

Abstract: This Community Service Program (PkM) focuses on delivering fundamental web security concepts tailored to the age-specific needs of young learners. Collaborative efforts among teachers, and parents, ensure a cohesive implementation of web security measures within the school environment. This PkM activity aims to enhance elementary school student's knowledge about the importance of web security. The method used is by giving lectures and interactive discussions to PkM participants. The total number of participants involved in this PkM is around 25 people, consisting of teachers and students. Evaluation of this PkM activities based on questionnaire feedback from PkM participants. According to feedback from participants, there has been a noticeable improvement in understanding the significance of web security. Before engaging in PkM activities, only approximately 5 percent of students possessed knowledge regarding the importance of web security. However, post-PkM activities, approximately 90 percent of students affirmed their awareness of the significance of web security. 
Deteksi Serangan Denial of Service pada Internet of Things Menggunakan Finite-State Automata Fery Antony; Rendra Gustriansyah
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 21 No. 1 (2021)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v21i1.1078

Abstract

Internet of things memiliki kemampuan untuk menghubungkan obyek pintar dan memungkinkan mereka untuk berinteraksi dengan lingkungan dan peralatan komputasi cerdas lainnya melalui jaringan internet. Namun belakangan ini, keamanan jaringan internet of things mendapat ancaman akibat serangan cyber yang dapat menembus perangkat internet of things target dengan menggunakan berbagai serangan denial of service. Penelitian ini bertujuan untuk mendeteksi dan mencegah serangan denial of service berupa synchronize flooding dan ping flooding pada jaringan internet of things dengan pendekatan finite-state automata. Hasil pengujian menunjukkan bahwa pendekatan finite-state automata berhasil mendeteksi serangan synchronize flooding dan ping flooding pada jaringan internet of things, tetapi pencegahan serangan tidak secara signifikan mengurangi penggunaan prosesor dan memori. Serangan synchronize flooding menyebabkan delay saat mengaktifkan/menonaktifkan peralatan internet of things sedangkan serangan ping flooding menyebabkan error. Implementasi bash-iptables berhasil mengurangi serangan synchronize flooding dengan efisiensi waktu pencegahan sebesar 55,37% dan mengurangi serangan ping flooding sebesar 60% tetapi dengan waktu yang tidak signifikan.
Penyuluhan aman dalam berbisnis pada usaha kue dan snack di Keluruahan Talang Jambe Palembang Ahmad Sanmorino; Rendra Gustriansyah; Shinta Puspasari
SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Vol 9, No 4 (2025): Juli
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jpmb.v9i4.31489

Abstract

AbstrakMaraknya penipuan digital melalui WhatsApp dan media sosial menjadi ancaman serius bagi pelaku usaha kue dan snack rumahan di Kelurahan Talang Jambe, Palembang. Kegiatan Pengabdian kepada Masyarakat (PkM) ini bertujuan meningkatkan literasi digital pelaku usaha dalam mengidentifikasi dan mencegah modus penipuan daring. Metode yang digunakan meliputi presentasi materi dan diskusi interaktif, dengan pendekatan berbasis teori Digital Literacy dan Technology Acceptance Model (TAM). Kegiatan ini dilaksanakan pada Mei 2025 dengan melibatkan pelaku UMKM lokal. Hasil evaluasi menunjukkan peningkatan pemahaman peserta terhadap berbagai jenis penipuan, dari rata-rata 65% sebelum kegiatan menjadi 90,8% setelahnya. Diskusi juga mengungkap pengalaman peserta yang sebelumnya nyaris menjadi korban penipuan. Temuan ini menunjukkan bahwa pendekatan edukatif langsung efektif meningkatkan kesadaran dan kesiapsiagaan digital pelaku usaha. Diharapkan kegiatan ini dapat direplikasi untuk memperkuat keamanan digital UMKM secara lebih luas. Kata kunci: keamanan digital; usaha kue dan snack; penipuan online; pengabdian kepada masyarakat. AbstractThe rise of digital fraud through WhatsApp and social media poses a serious threat to home-based cake and snack businesses in Talang Jambe, Palembang. This Community Service (PkM) initiative aimed to enhance digital literacy among business owners by equipping them with practical knowledge to identify and prevent common online scams. The program employed presentations and interactive discussions, guided by the frameworks of Digital Literacy and the Technology Acceptance Model (TAM). Conducted in May 2025, the activity engaged local micro-entrepreneurs who actively use digital platforms for marketing. Evaluation results showed a significant increase in participants’ understanding of various types of fraud—from an average of 65% before the program to 90.8% afterward. Discussions revealed that several participants had previously been close to falling victim to such scams. These findings highlight the effectiveness of direct educational approaches in strengthening cybersecurity awareness. The program is expected to serve as a model for similar efforts aimed at improving the digital resilience of MSMEs. Keywords: digital security; cake and snack business; online fraud; community service.
Analisis Sentimen Terhadap Opini Publik Tentang Kebijakan Regulasi Kripto Di Indonesia Menggunakan Metode Regresi Logistik Nazka yasidi; Rendra Gustriansyah; Lastri Widya Astuti
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 2 (2025): Agustus: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i2.5733

Abstract

This study investigates public sentiment toward cryptocurrency regulation policies in Indonesia by employing a logistic regression approach on social media data. A total of 300 Indonesian-language tweets were collected from platform X between January 2022 and April 2025 through a web scraping method using targeted keywords related to cryptocurrency payment regulations. Data preprocessing included text cleaning, case folding, stemming with the Sastrawi library, stopword removal, and tokenization, followed by feature extraction using TF-IDF. Sentiment labels were manually assigned in collaboration with legal experts to ensure classification accuracy. The logistic regression model achieved strong predictive performance, with 91.67% accuracy on the test set and stable results across K-Fold Cross Validation, yielding an average accuracy of 92–93%. The sentiment analysis revealed that the majority of public opinion expressed positive sentiment (85%), while negative sentiment represented only 15%. Positive sentiment was primarily associated with terms such as “protect,” “regulate,” “benefit,” and “legality,” highlighting public support for regulatory measures that enhance investor protection and provide legal certainty. Conversely, negative sentiment featured terms including “forbidden,” “restrict,” and “obstruct,” which reflected concerns regarding regulatory barriers and religious considerations surrounding cryptocurrency usage. The findings demonstrate that Indonesian society generally perceives cryptocurrency regulation as a constructive initiative toward building a secure and trustworthy digital asset ecosystem. Furthermore, the empirical evidence contributes to the growing literature on public perception of financial technology regulations in developing countries. For policymakers, the results emphasize the importance of transparent communication and balanced regulatory frameworks to maintain public trust while addressing potential risks. Overall, this research provides valuable insights into how sentiment analysis can inform the design of more effective regulatory strategies in the evolving landscape of digital finance.
Analisis Sentimen Kepuasan Pengguna Lintas Rel Terpadu (LRT) menggunakan Metode Support Vector Machine Rangga Febri Kasih; Rendra Gustriansyah; Zaid Romegar Mair
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 2 (2025): Agustus: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i2.5832

Abstract

This study aims to analyze public sentiment toward the Palembang LRT service by utilizing user reviews available on the Google Maps platform. Sentiment analysis was conducted to understand public perceptions of service quality, which can serve as a basis for decision-making in improving public transportation services. The method employed in this research is the Support Vector Machine (SVM) algorithm combined with Term Frequency-Inverse Document Frequency (TF-IDF) for word weighting, which classifies reviews into two sentiment categories: positive and negative. A total of 500 reviews were randomly selected as the dataset and processed through a text preprocessing stage, including data cleaning, tokenization, and stopword removal to enhance data quality. The SVM model was then evaluated using an 80:20 split for training and testing, achieving an accuracy of 91%, which indicates excellent performance in identifying sentiment patterns in the Indonesian language. The findings of this study confirm that SVM-based approaches are effective and reliable for sentiment analysis in the context of public transportation. These results provide practical contributions for Palembang LRT management, as insights into public sentiment can be used as a strategic reference for decision-making, reputation management, and improving service quality based on user needs. Future research is recommended to expand the dataset, include neutral sentiment categories, and compare SVM performance with other machine learning algorithms to achieve more comprehensive and robust results.
Metode Pembelajaran Mesin untuk Memprediksi Status Gizi Balita Rendra Gustriansyah; Nazori Suhandi; Shinta Puspasari; Ahmad Sanmorino
JURNAL INFOTEL Vol 16 No 1 (2024): February 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v15i4.988

Abstract

Malnutrition is one of the leading health problems experienced by toddlers in various countries. Based on the 2022 Indonesian Nutritional Status Survey results, malnutrition in children under five in Indonesia is higher than the average malnutrition in Africa and globally. Therefore, a way is needed to predict the nutritional status of children under five early and quickly so that the Government (through District Health Office) can immediately provide the necessary treatment. This study aims to predict or classify the toddlers' nutritional status based on age, body mass index (BMI), weight, and body length using various machine learning (ML) methods, namely naïve Bayes, linear discriminant analysis, decision tree, k-nearest neighbor, random forest, and support vector machine. The predictive performance of each ML method was evaluated based on accuracy, sensitivity, specificity, the area under curve, and Cohen's Kappa coefficient. The test results show that the RF method is the most recommended for predicting toddlers' nutritional status. The study's contribution is to obtain information about toddlers' nutritional status easier.
Klasifikasi Penyakit TBC Menggunakan Metode UMAP dan K-NN Nazori Suhandi; Rendra Gustriansyah; Abel Destria
bit-Tech Vol. 7 No. 3 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v7i3.2227

Abstract

Tuberkulosis (TBC) adalah penyakit infeksi yang disebabkan oleh bakteri Mycobacterium tuberculosis, yang dapat menyebar dengan cepat melalui udara. Deteksi dini yang akurat sangat penting dalam penanganan penyakit ini untuk mencegah penyebaran lebih lanjut serta meningkatkan efektivitas pengobatan. Diagnosis yang tidak tepat dapat menyebabkan keterlambatan dalam pengobatan, sehingga meningkatkan risiko komplikasi serius bagi pasien. Oleh karena itu, penelitian ini bertujuan untuk mengembangkan sistem klasifikasi TBC menggunakan metode Uniform Manifold Approximation and Projection (UMAP) dan K-Nearest Neighbors (K-NN) di Puskesmas Prabumulih Timur. Dataset yang digunakan terdiri dari 278 data pasien dengan berbagai atribut klinis terkait gejala TBC. Proses analisis diawali dengan tahap pra-pemrosesan data, termasuk penghapusan data duplikat, encoding data kategorikal, serta penanganan nilai yang hilang. Untuk meningkatkan akurasi klasifikasi, metode Elbow diterapkan guna menentukan nilai K optimal, dengan hasil terbaik pada K=3. Data kemudian dibagi menjadi 80% data pelatihan dan 20% data uji guna menghindari overfitting dan meningkatkan reliabilitas model. Pengujian dilakukan dengan membandingkan dua skenario, yaitu K-NN tanpa UMAP dan K-NN dengan UMAP. Hasil evaluasi menggunakan Confusion Matrix menunjukkan bahwa penerapan UMAP meningkatkan accuracy dari 93,48% menjadi 100%, dengan precision dan recall juga mencapai nilai maksimal. Penelitian ini berkontribusi dalam pengembangan sistem klasifikasi berbasis machine learning yang lebih akurat dan efisien untuk membantu tenaga medis dalam mendiagnosis TBC secara cepat, tepat, dan optimal dalam sistem layanan kesehatan.
Predicting Precious Metal Prices Using the Long-Short-Term Memory (LSTM) Method Marshanda Amalia Vega; Rendra Gustriansyah; Indah Permatasari
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.2985

Abstract

Gold price fluctuations pose significant challenges for investors in determining accurate investment strategies. The volatility is strongly influenced by inflation, exchange rates, and global economic dynamics, making reliable forecasting increasingly important. Although various statistical and machine learning models have been applied, many are limited in capturing complex temporal dependencies, especially in the context of Indonesia’s ANTAM gold prices. This study addresses that gap by applying the Long Short-Term Memory (LSTM) method, a deep learning approach designed to model sequential patterns in time series data. The novelty of this research lies in the application of LSTM specifically for ANTAM gold price forecasting in Indonesia, which has received limited attention in previous studies. Unlike conventional approaches, LSTM is capable of preserving long-term dependencies, thereby improving predictive accuracy for volatile commodities. Using historical daily data from November 2023 to March 2025, the model was trained to recognize price dynamics and evaluated with Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The results demonstrate high predictive accuracy, with a MAPE of 1.39% and RMSE of 0.0137. These findings confirm the suitability of LSTM for gold price prediction and underline its potential contribution to both theoretical advancements in time series forecasting and practical decision-making in investment management. Thus, this study not only strengthens evidence of LSTM’s effectiveness but also offers valuable insights for investors and policymakers in managing risks associated with commodity price volatility.
Co-Authors Abel Destria Ade Fathurahman Adhe Suryani Agung Prakesakwa Ahmad Sanmorino Aldi Saputra Amalia, Marshanda Aminuyati Andri Agus Agus S Antony, Fery Apriano Putri, Aurahaqqi Ariati, Nining Arief Ramadhan Aries Fadilla, Muhammad Bagas Ario Pratama, Surya Cahyani, Septa Dana Indra Sensuse Darius Antoni Desfourtheen, Rinda Destria, Abel Dewi Sartika Dewi Sartika DEWI SARTIKA Dewi, Deshinta Arrova Dhamayanti Dhamayanti Dhamayanti, Dhamayanti Ditho Hersilava Dwi Asa Verano Ermatita - Fachry Abda El Rahman Febrianty Fery Antony Fery Antony Filian Falanda Hartini Hartini Hartini Hartini Hastha Sunardi Haversyalapa, Ditho Indah Permatasari Isabella, Isabella Juhaini Alie Kris, Via Lastri Widya Astuti M. Ricky Hikmawan Maharani, Aiga Rizki Marshanda Amalia Vega Megat Norulazmi Megat Mohamed Noor MILLANO, FIDO Mitha Sofiana Mufti, Nabilah Muhammad Farhan Muhammad Haviz Irfani Nadia Damayanti Nazka yasidi Nazori Suhandi Pratama, M Duta Purbasari, Nadira Putri Purnamasari, Evi Rangga Febri Kasih Reza Iman Pribadi Rudi Heriansyah Ryan Ramanda Putra Sari, Nursella Sartika, Dewi Shinta Puspasari Shinta Puspasari Suryati Suryati Suryati Suryati Terttiaavini, Terttiaavini Verano, Dwi Asa Wilza Nadia Wiyanto, Ari Zulkifli Zulkifli