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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 Teknosains : Jurnal Sains,Teknologi dan Informatika 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 Automotive Engineering 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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Improving Information Security with Machine Learning Sanmorino, Ahmad; Gustriansyah, Rendra; Puspasari, Shinta; Alie, Juhaini
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 1 (2024): Article Research Volume 6 Issue 1, January 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i1.3317

Abstract

The study Improving Information Security with Machine Learning explores the fusion of machine learning methodologies within information security, aiming to fortify conventional protocols against evolving cyber threats. By conducting a comprehensive literature review and empirical analysis, this scholarly endeavor highlights the efficacy of machine learning in anomaly detection, threat identification, and predictive analytics within security frameworks. Through practical demonstrations, such as z-score-based anomaly detection in network traffic data and NLP-based email security systems, the study illustrates the practical applications of machine learning techniques. Additionally, it delves into the mathematical underpinnings of predictive analytics and the architecture of neural networks for malware detection. However, while showcasing the transformative potential of machine learning, the study also confronts significant challenges. Ethical, legal, and privacy considerations emerge prominently, emphasizing the need for regulations addressing algorithmic biases, ethical dilemmas, and data protection. Moreover, the study emphasizes the practical challenges of scalability, interpretability, continual adaptation to evolving threats, and the harmonious interaction between human expertise and machine intelligence. By offering practical recommendations and future research directions, this scholarly exploration aims to empower researchers, practitioners, and policymakers in navigating the complex intersection of machine learning and information security, thereby fostering innovation and comprehension in this evolving domain.
Toddlers’ Nutritional Status Prediction Using the Multinomial Logistics Regression Method Gustriansyah, Rendra; Suhandi, Nazori; Puspasari, Shinta; Sanmorino, Ahmad; Sartika, Dewi
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 1 (2024): Article Research Volume 6 Issue 1, January 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i1.3372

Abstract

Malnutrition is one of the foremost health problems experienced by children under five in many countries, especially in low and middle-income countries. Meanwhile, the target of Sustainable Development Goals (SDGs) 2.2 is that all forms of malnutrition must end by 2025. Therefore, this study aims to predict the toddlers’ nutritional status (malnutrition, undernutrition, overnutrition, and normal nutrition) based on age, body mass index (BMI), weight, and length using the Multinomial Logistic Regression (MLR) classification method. The dataset consists of two hundred toddlers obtained from the Kaggle site. Following pre-processing, the dataset is divided, with 80 percent of the data for training and the remaining 20 percent for testing. The model was trained using 10-fold cross-validation (CV). In Addition, the MLR model performance was evaluated using the confusion matrix (CM), the area under the curve (AUC), and the Kappa coefficient (KC). The evaluation results using CM show that the accuracy, sensitivity, and specificity values are 0.9412, 0.9375, and 0.9790, respectively. AUC and KC also show excellent results. It indicates that the MLR method is an esteemed and recommended method for predicting the nutritional status of toddlers. Therefore, this research can contribute to providing early information so that the Government can immediately determine the necessary treatment.
Klasifikasi Penyakit TBC Menggunakan Metode UMAP dan K-NN Nazori Suhandi; Rendra Gustriansyah; Destria, Abel
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.
Pendampingan Implementasi E-Arsip Untuk Proyek Infrastruktur Tol Gustriansyah, Rendra; Suhandi, Nazori; Puspasari, Shinta; Sanmorino, Ahmad; Wiyanto, Ari
Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal Vol 8, No 2 (2025): April 2025
Publisher : STMIK Royal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurdimas.v8i2.3605

Abstract

The conventional management of archives using physical files at the Jambi-Betung II Toll Road Land Procurement Commitment Making Officer (PPK-PPTJT) agency results in slow document retrieval, a higher risk of data loss, and limited accessibility to important information. This community service initiative aims to enhance the technological skills of human resources at PPK-PPTJT Jambi-Betung II, particularly in electronic archive management. The methods employed involve socialization and technical training on using e-archive applications for four PPK-PPTJT employees. Evaluation was conducted through questionnaires and interviews to assess the participants' improvement in understanding and skills. The results demonstrated a significant increase in participants' capabilities: 25% reported a better understanding of the benefits of e-archives, 75% enhanced their operational application skills, and 25% felt more confident in managing electronic archives. The implementation of e-archives has successfully reduced reliance on physical documents and expedited the toll road land procurement administration process, ultimately increasing the operational efficiency of PPK-PPTJT Jambi-Betung II.Keywords: e-archive; mentoring; land procurement; archive management  Abstrak: Pengelolaan arsip secara konvensional dengan menggunakan berkas fisik di instansi Pejabat Pembuat Komitmen Pelaksana Pengadaan Tanah Jalan Tol (PPK-PPTJT) Jambi-Betung II menyebabkan lambatnya pencarian dokumen, rentan kehilangan data, dan terbatasnya aksesibilitas terhadap informasi penting. Tujuan pengabdian ini adalah untuk meningkatkan kapasitas sumber daya manusia di PPK-PPTJT Jambi-Betung II dalam hal digitalisasi dan pengelolaan arsip elektronik. Metode yang digunakan meliputi sosialisasi, pelatihan teknis penggunaan aplikasi e-arsip bagi empat pegawai PPK-PPTJT. Evaluasi dilakukan dengan angket dan wawancara untuk mengukur peningkatan pemahaman dan keterampilan empat peserta. Hasil evaluasi menunjukkan peningkatan signifikan: 19% peserta lebih memahami manfaat e-arsip, 31% peningkatan kemampuan operasional aplikasi, dan 25% peningkatan kepercayaan diri dalam pengelolaan arsip elektronik. Penerapan e-arsip berhasil mengurangi ketergantungan pada arsip fisik, mempercepat proses administrasi pengadaan tanah jalan tol, efisiensi ruang penyimpan, dan kemudahan monitoring dan evaluasi proses operasional PPK-PPTJT Jambi-Betung II.Kata kunci: e-arsip; pendampingan; pengadaan tanah; pengelolaan arsip
MEASURING PERCEIVED USABILITY OF ARTIFICIAL INTELLIGENCE-BASED QUIZZES IN A VIRTUAL MUSEUM Shinta Puspasari; Rendra Gustriansyah; Dwi Asa Verano; Ahmad Sanmorino; Hartini Hartini; Ermatita Ermatita
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 4 (2025): JITK Issue May 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i4.5611

Abstract

The transformation of modern museums through digital technology offers added value to visitors, especially in the context of education. Virtual museums, in particular, complement physical museums by providing accessibility and enhancing the learning experience. The SMBII virtual museum includes an AI-based quizzes feature designed to assess the knowledge level of visitors regarding the museum's history and collections as an educational feature. In addition to physical museums, virtual museums offer convenience and enrich the learning process for visitors. The quizzes adapts its questions based on the visitor's profile, leveraging AI to tailor content and maximize learning outcomes. This study aims to compare the effectiveness of two widely used usability metrics—System Usability Scale (SUS) and Usability Metric for User Experience (UMUX)—in evaluating the usability of the AI-driven quiz feature within the SMBII virtual museum. The study specifically seeks to determine whether there are significant differences between SUS and UMUX in measuring user perceptions of the quiz’s usability. The primary respondents of this study were students, who represent the museum's target audience for educational purposes. Hypothesis testing results show no significant difference between the SUS and UMUX scores (P > 0.05), indicating that both metrics offer similar evaluations of usability. Based on these findings, the study recommends the use of UMUX over SUS for future usability assessments in virtual museum systems, as UMUX is more time-efficient without compromising accuracy. This research contributes to advancing the understanding of usability testing methods for AI-based educational features in virtual museum environments
PENINGKATAN PENGETAHUAN MASYARAKAT LEWAT PEMANFAATAN APLIKASI VIRTUAL TOUR 360 MUSEUM SMBII DI MASA PANDEMI Puspasari, Shinta; Dhamayanti, Dhamayanti; Gustriansyah, Rendra; Verano, Dwi Asa; Sanmorino, Ahmad
Reswara: Jurnal Pengabdian Kepada Masyarakat Vol 4, No 1 (2023)
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/rjpkm.v4i1.2493

Abstract

Museum SMBII memiliki fungsi menyimpan, memelihara, memamerkan koleksi benda bersejarah dan budaya Palembang untuk tujuan rekreasi maupun edukasi yang dimanfaatkan seluas-luasnya bagi masyarakat khususnya kota Palembang. Namun, saat pandemi COVID-19 melanda dunia, kebijakan pemerintah daerah Palembang menetapkan museum SMBII untuk menerapkan kebijakan yang disesuaikan dengan situasi pandemi. Museum terpaksa tutup dan membatasi akses bagi aktivitas fisik di museum.  Pengelola museum SMBII telah menyediakan aplikasi untuk memudahkan masyarakat mengakses museum secara virtual. Untuk lebih memperkenalkan aplikasi tersebut, kegiatan pengenalan pemanfaatan aplikasi virtual tour 360 museum SMBII dilaksanakan dengan peserta pelajar atau mahasiswa yang merupakan kategori pengunjung dominan dari museum SMBII Palembang sebelum pandemi. Peserta dikenalkan fungsionalitas dan cara pemanfaatan tiap fitur aplikasi. Peserta diminta menggunakan aplikasi dan mengisi kuesioner evaluasi tingkat penerimaan peserta terhadap aplikasi. Hasil kuesioner menunjukkan bahwa pengetahuan peserta rata-rata meningkat dan aplikasi virtual tour 360 mudah digunakan serta meningkatkan minat peserta untuk jelajah wisata budaya Palembang
Single Exponential Smoothing Method to Predict Sales Multiple Products Gustriansyah, Rendra
International Series on Interdisciplinary Science and Technology Vol. 3 No. 2 (2018)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/ins.v3i2.176

Abstract

—Activity to predict sales multiple products intended for control of the number of existing stock, so the lack or excess stock can be minimized. When the number of sales can be accurately predicted, then the fulfilment of consumer demand can be cultivated in a timely and cooperation with suppliers maintained properly so that company can avoid losing sales and customers. This study aims to predict sales multiple products (6,877 products) using Single Exponential Smoothing (SES) approach, which is expected to improve the efficiency of the inventory system. Measurement accuracy of prediction in this study using a standard measurement Mean Absolute Percentage Error (MAPE), which is the most important criteria in analyzing the accuracy of the prediction. The results showed that the average of percentage prediction error of products using SES is high, because MAPE value obtained is 1.056% with a smoothing parameter α = 0.9
Marketing Strategy Using Frequent Pattern Growth Suhandi, Nazori; Gustriansyah, Rendra
Journal of Computer Networks, Architecture and High Performance Computing Vol. 3 No. 2 (2021): Journal of Computer Networks, Architecture and High Performance Computing, July
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v3i2.1039

Abstract

The biggest problem faced by printing companies during the Covid-19 pandemic was that the number of orders was unstable and tends to decrease, which had the potential to harm the company. Therefore, various appropriate marketing strategies were needed so that the number of product orders was relatively stable and even increases. The impact was that the company could survive and continued to grow. This study aimed to assist company managers in developing appropriate marketing strategies based on association rules generated from one of the data mining methods, namely the Frequent Pattern Growth (FP-Growth) method. The case study of this research was a printing company where there was no similar research that used a printing company's dataset. This study produced nine association rules that meet a minimum of 25% support and a minimum of 60% confidence, but only two association rules that had a high positive correlation, namely for a custom paper bag and banner products. Therefore, several marketing strategies were suggested that could be used as guidelines for companies in managing sales packages and giving special discounts on a product. The results of this study are expected to trigger an increase in the number of product orders because this study tried to find the right product for consumers and did not try to find the right consumers for a product.
Sosialisasi Augmented Reality Koleksi Kain Tradisional Museum Songket Palembang Puspasari, Shinta; Gustriansyah, Rendra; Sanmorino, Ahmad; Purbasari, Nadira Putri; Farhan, Muhammad
Jurnal Abdimas Mandiri Vol. 9 No. 2
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jam.v9i2.5804

Abstract

Museum Songket di Palembang merupakan institusi budaya yang memiliki peran penting dalam pelestarian kain tradisional khas Sumatera Selatan, khususnya kain Songket yang kaya nilai sejarah dan estetika. Namun, metode penyampaian informasi koleksi yang masih bersifat konvensional sering kali kurang menarik bagi pengunjung, terutama generasi muda. Kegiatan Pengabdian kepada Masyarakat ini bertujuan untuk mensosialisasikan pemanfaatan teknologi Augmented Reality (AR) sebagai media interaktif dalam memperkenalkan motif-motif Songket koleksi Museum Songket Zainal dan Museum Sultan Mahmud Badaruddin II Palembang. Aplikasi AR yang dikembangkan menampilkan visualisasi motif kain berbasis marker, dilengkapi dengan deskripsi dan fitur kuis untuk mengukur pemahaman pengguna. Sosialisasi dilakukan secara langsung kepada pengunjung museum dan secara daring kepada masyarakat umum. Hasil evaluasi menggunakan instrumen System Usability Scale (SUS) menunjukkan skor rata-rata sebesar 71 dari 30 responden, yang mengindikasikan tingkat usabilitas aplikasi yang baik. Penggunaan teknologi AR terbukti mampu meningkatkan pemahaman dan minat pengunjung terhadap warisan budaya kain tradisional khusunya songket, sekaligus menjadi solusi pelestarian koleksi yang sudah rapuh tanpa kontak fisik langsung. Kegiatan ini mendorong pengelola museum untuk mempertimbangkan adopsi teknologi digital sebagai bagian dari strategi edukasi dan pelestarian budaya yang lebih adaptif di era digital. 
Analisis Perbandingan PCA-KNN dan SVM untuk Prediksi Risiko Diabetes Desfourtheen, Rinda; Damayanti, Nadia; Gustriansyah, Rendra
Jurnal Sarjana Teknik Informatika Vol. 13 No. 3 (2025): Oktober
Publisher : Program Studi Informatika, Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/jstie.v13i3.31232

Abstract

Diabetes merupakan penyakit kronis yang sering terlambat terdiagnosis akibat gejala awal yang tidak spesifik, sehingga deteksi dini penting untuk mencegah komplikasi serius. Penelitian ini bertujuan menganalisis dan membandingkan performa kombinasi Principal Component Analysis dengan K-Nearest Neighbor (PCA-KNN) dan Support Vector Machine (SVM) dalam prediksi risiko diabetes. Dataset yang digunakan berasal dari Kaggle dengan 768 entri dan delapan atribut medis. Tahap praproses mencakup imputasi median untuk nilai nol, normalisasi Z-score, serta reduksi dimensi menggunakan PCA pada model KNN yang menghasilkan lima komponen utama dengan varian kumulatif >80%. Nilai k optimal ditentukan melalui 10-Fold Cross Validation dengan hasil terbaik pada k=16. Hasil evaluasi menunjukkan PCA-KNN mencapai akurasi 76,47%, sensitivitas 90,00%, dan spesifisitas 50,94%, lebih baik dibanding KNN standar. Sementara itu, SVM memperoleh akurasi 72,73% dengan spesifisitas tinggi (84,00%) namun sensitivitas rendah (51,85%). Temuan ini mengindikasikan bahwa PCA-KNN lebih sesuai untuk skrining awal karena sensitivitas tinggi, sedangkan SVM dapat digunakan pada tahap konfirmasi berkat spesifisitas yang lebih baik.
Co-Authors 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 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 Isabella, Isabella Juhaini Alie Kris, Via Lastri Widya Astuti M. Ricky Hikmawan Maharani, Aiga Rizki Megat Norulazmi Megat Mohamed Noor MILLANO, FIDO Mitha Sofiana Mufti, Nabilah Muhammad Farhan Muhammad Haviz Irfani Nadia Damayanti Nazka yasidi Nazori Suhandi 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