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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) Jurnal Penelitian Pendidikan IPA (JPPIPA) Journal of Computer Science and Informatics Engineering (J-Cosine) Jurnal Teknologi Informasi, Komputer, dan Aplikasinya (JTIKA ) Jurnal Teknik Informatika (JUTIF) Jurnal Begawe Teknologi Informasi
Ario Yudo Husodo, Ario Yudo
Departmet of Informatics Engineering, Engineering Faculty, Mataram University
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Materials Science & Nanotechnology Physics

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Analisis Penggunaan Sensor Kamera 3 Dimensi Kinect sebagai Media Pembelajaran Perkuliahan berbasis Student Centered Learning Husodo, Ario Yudo; Bimantoro, Fitri; Arimbawa, I Wayan Agus; Afwani, Royana
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 5, No 1 (2019): Volume 5 No 1
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (964.367 KB) | DOI: 10.26418/jp.v5i1.29893

Abstract

Saat ini, kegiatan belajar mengajar di berbagai universitas di Indonesia umumnya dilakukan menggunakan laptop yang terhubung ke sebuah proyektor. Di dalam proses pengajaran, dosen cenderung menggunakan laptop di ruang kelas, menghubungkan tampilan layar laptop ke sebuah proyektor, kemudian menggunakan wireless pointer sebagai pengendali slide presentasi. Pendekatan semacam ini sayangnya tergolong kurang efektif di dalam perkuliahan berbasis Student Centered Learning (SCL). Di dalam SCL, mahasiswa merupakan pusat kegiatan perkuliahan, dimana keterlibatan dan interaksi mahasiswa di dalam proses penyampaian materi merupakan hal terpenting guna meningkatkan animo dan pemahaman mahasiswa mempelajari suatu topik. Penggunaan teknologi perkuliahan konvensional seperti laptop dan wireless pointer cenderung membuat perkuliahan menjadi 1 arah karena interaksi mahasiswa kurang dapat tersalurkan. Di dalam penelitian ini, dilakukan analisis dampak penggunaan teknologi informasi sensor kamera 3 dimensi Kinect sebagai media pembelajaran dalam suatu sistem Smart Classroom. Kinect merupakan sensor yang dapat menangkap gerakan tangan seseorang kemudian menginterpretasikan gerakan tersebut sebagai suatu perintah komputasi berdasarkan algoritma pemrograman tertentu. Berdasarkan hasil penelitian, penggunaan Kinect terbukti dapat meningkatkan efektivitas pembelajaran SCL dan antusiasme belajar mahasiswa karena mahasiswa dapat berinteraksi di dalam suatu materi perkuliahan secara lebih alami menggunakan gerakan tangan.
Prototype Early Warning System Tanah Longsor Menggunakan Fuzzy Logic Berbasis Google Maps Sugianti, Novalia Dwirohmatun; Widiartha, Ida Bagus Ketut; Husodo, Ario Yudo
Journal of Computer Science and Informatics Engineering (J-Cosine) Vol 3 No 2 (2019): December 2019
Publisher : Informatics Engineering Dept., Faculty of Engineering, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1108.717 KB) | DOI: 10.29303/jcosine.v3i2.273

Abstract

Landslide is one of frequent natural disaster in Indonesia that can cause many casualties, damage the buildings, loss of livelihoods, deteriorating sanitation, and the emergence of many diseases. To minimize this, prevention is needed by mapping the landslide prone areas and provided the early warning. This research will test in six areas in Lombok there are Batulayar, Bayan, Tanjung, Gangga, Sambelia, and Sembalun. The variable used are ground height, slope, rainfall, soil type, and land cover. There is a lot of method that can use to mapping such as fuzzy logic. Fuzzy logic is one of method that can mapping the input into the output space. By using fuzzy logic produces a level of accuracy for determining landslide prone areas is 83,3% and for the warning is 91,67%. So that, fuzzy logic can be used to mapping the landslide prone areas and make the early warning sytem.
Pelatihan Desain Grafis Untuk Masyarakat Pelaku Wisata Di Lombok: Graphic Design Training for Tourism Communities In Lombok Widiartha, Ida Bagus Ketut; Afwani, Royana; Bimantoro, Fitri; Husodo, Ario Yudo; Agitha, Nadiyasari
Jurnal Begawe Teknologi Informasi (JBegaTI) Vol. 4 No. 2 (2023): JBegaTI
Publisher : Program Studi Teknik Informatika, Fakultas Teknik Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jbegati.v4i2.1113

Abstract

Lombok merupakan salah satu tujuan wisata utama yang ada di Indonesia, keindahan alamnya tidak kalah dengan Bali yang sudah terlebih dahulu terkenal di Manca Negara. Keberagaman budaya dan produk lokal yang dimiliki juga sangat banyak dan mendukung peningkatan jumlah kunjungan wisata. Promosi yang dilakukan oleh pemerintah tidak dapat mengakomodir semua obyek wisata dan mempromosikan produk lokal dari masyarakat tersebut, karena seiring dengan kesadaran masyarakat tentang pentingnya pariwisata dalam meningkatkan kesejahteraan, banyak sekali muncul obyek wisata baru dan produk- produk baru yang tidak tersentuh oleh promosi pemerintah. Dan promosi yang dilakukan secara konvensional membutuhkan biaya yang sangat mahal. Diera digital saat ini media sosial, memegang peranan sangat penting dalam mempromosikan sesuatu. Selain kemampuannya untuk mempromosikan produk ataupun obyek, media sosial juga bisa digunakan untuk membuat personal branding yang pada hilirnya dapat mendatangkan keuntungan materi. Untuk membuat konten yang menarik perlu pelatihan ketrampilan kepada masyarakat untuk dapat meningkatkan kemampuannya dalam membuat konten yang ditampilkan dalam media sosial sehingga lebih banyak orang melihat dan berkomentar yang pada akhirnya dapat menjadi media promosi yang murah
PERFORMANCE COMPARISON OF NAIVE BAYES AND BIDIRECTIONAL LSTM ALGORITHMS IN BSI MOBILE REVIEW SENTIMENT ANALYSIS Ma'we, Hannatul; Husodo, Ario Yudo; Irmawati, Budi
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.2024.5.6.4178

Abstract

Currently, almost all banks have used mobile banking in conducting banking transactions, one of which is Bank Syariah Indonesia (BSI). BSI mobile is still classified as a new mobile banking application compared to other mobile banking, this certainly still has a low rating and really needs feedback from users which can be seen through reviews on the Google Play Store application. Input in the form of criticism and suggestions from BSI mobile users can be used by BSI mobile as a suggestion for careful supervision and evaluation material in improving its services. This study aims to find the best algorithm to analyze review sentiment on the Google Play Store for the BSI mobile application and provide an overview of the response of application users to application developers based on the results of review data processing. The data mining methodology used in this study is CRISP-DM, using a dataset collected for 6 years (2018-2023) which is annotated into positive and negative labels manually, then modeled using 2 algorithms, namely Naïve Bayes (NB) and Bidirectional LSTM (BiLSTM). The contribution of this study is to test, evaluate and compare the two algorithms (NB and BiLSTM) using the K-Fold Cross Validation (NB) testing model and over-sampling techniques to the minority class (negative) then provide recommendations for the best algorithm. The conclusion of the study is that the BiLSTM algorithm is superior to NB with an accuracy of 94.90 % while the NB algorithm is 94%. In addition, the over-sampling technique is more optimal in increasing the accuracy of the algorithm's performance compared to without over-sampling.
IMPROVING SHOPPING EXPERIENCES AT NTB MALL THROUGH PERSONALIZED PRODUCT RECOMMENDATIONS USING CONTENT-BASED FILTERING Husodo, Ario Yudo; Bimantoro, Fitri; Agitha, Nadiyasari; Grendis, Nuraqilla Waidha Bintang
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.2025.6.1.4194

Abstract

NTB MALL, an e-commerce platform specializing in unique products from micro, small, and medium enterprises (MSMEs) in West Nusa Tenggara, faces challenges in providing personalized product recommendations due to the diversity of its product categories and consumer preferences. To address this, this study implements a content-based filtering (CBF) approach utilizing Term Frequency-Inverse Document Frequency (TF-IDF) and cosine similarity to enhance recommendation accuracy. The system analyzes product attributes and user interaction history to generate tailored suggestions. Experimental results indicate that cosine similarity outperforms Euclidean distance in recommendation precision, achieving an accuracy of 89% and a Mean Reciprocal Rank (MRR) of 95%. Furthermore, user feedback reveals that 93% of users found the recommendations highly relevant, 89% reported increased engagement, and 96% expressed satisfaction with the personalized shopping experience. This research provides a novel application of AI-driven recommendation systems in regional e-commerce marketplaces, demonstrating their potential to improve user experience and foster stronger connections between consumers and local producers.
Digital Business Model Training to Support the Development of a Modern Market for Local Street Vendors and Tourism Awareness Groups in the Mandalika Special Economic Zone: PELATIHAN MODEL BISNIS DIGITAL UNTUK MENDUKUNG TERCIPTANYA PASAR MODERN BAGI PEDAGANG KAKI LIMA DAN KELOMPOK SADAR WISATA PADA KAWASAN EKONOMI KHUSUS MANDALIKA Agitha, Nadiyasari; Husodo, Ario Yudo; Bimantoro, Fitri; Widiartha, Ida Bagus Ketut; Murpratiwi, Santi Ika
Jurnal Begawe Teknologi Informasi (JBegaTI) Vol. 6 No. 1 (2025): JBegaTI
Publisher : Program Studi Teknik Informatika, Fakultas Teknik Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jbegati.v6i1.1344

Abstract

Kawasan Ekonomi Khusus (KEK) Mandalika merupakan salah satu destinasi pariwisata prioritas yang mulai dikenal dunia karena penyelenggaraan MotoGP dan kegiatan balapan lainnya yang berstandar nasional maupun internasional. Dalam mendukung kegiatan tersebut, aktivitas jual beli berkembang pesat, terutama oleh Pedagang Kaki Lima (PKL). Namun, dalam praktiknya, beberapa PKL menjajakan dagangan mereka dengan cara memaksa dan kurang sopan, yang dapat mengganggu kenyamanan wisatawan serta menurunkan citra pariwisata KEK Mandalika. Untuk mengatasi masalah ini, dikembangkan model bisnis digital yang didukung oleh teknologi dari Dinas Perdagangan NTB, yaitu NTB Mall. Program pengabdian ini melibatkan beberapa tahapan: persiapan aplikasi NTB Mall, pelatihan literasi digital, pelatihan model bisnis digital, serta pelatihan pengelolaan pasar modern. Hasil pengabdian menunjukkan bahwa Kelompok Sadar Wisata (Pokdarwis) siap menjadi agent of change, terbukti dengan hasil pengujian System Usability Scale (SUS) sebesar 85, yang menunjukkan tingkat penerimaan aplikasi yang tinggi. Dampak lain yang terukur dari program ini mencakup peningkatan keterampilan digital bagi PKL, pemahaman strategi pemasaran digital, serta pertumbuhan ekonomi bagi para pedagang yang telah berpartisipasi. Selain itu, program ini juga berkontribusi dalam meningkatkan tata kelola kawasan KEK Mandalika dengan menata lokasi berdagang yang lebih rapi dan terorganisir, sehingga menciptakan lingkungan wisata yang lebih nyaman. Program ini juga mendorong peningkatan inklusi ekonomi bagi PKL melalui pemanfaatan teknologi digital, yang memungkinkan mereka untuk memperluas pasar dan meningkatkan daya saing di era digital.
KLUSTERING TOPIK PADA KOLOM KOMENTAR INSTAGRAM TENTANG KABINET MERAH PUTIH MENGGUNAKAN METODE K-MEANS Rahayu, Sefani Cahyo Auliya; Dwiyansaputra, Ramaditia; Husodo, Ario Yudo
JTIKA (Jurnal Teknik Informatika, Komputer dan Aplikasinya) Vol 7 No 1 (2025): Maret 2025
Publisher : Program Studi Teknik Informatika, Fakultas Teknik, Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jtika.v7i1.455

Abstract

This research attempts to determine the primary themes that Indonesians talked on President Prabowo Subianto's "Merah Putih" cabinet by using clustering analysis of Instagram comments. Using crawling data from the Instagram platform with the hashtag #KabinetMerahPutih, comments were gathered using the K-Means Clustering approach. Prior to the data being analyzed to create five clusters, the cleaning and pre-processing procedure, which included tokenization with IndoBERT and dimensionality reduction using Principal Component Analysis (PCA), was able to greatly improve the clustering quality, with the Silhouette Coefficient value rising from 0.010 to 0.200. Out of 23.780 initial data, 9.320 clean data were processed for this investigation. The findings demonstrate that the K-Means algorithm can group comments according to pertinent themes and offer profound understanding of support more responsive public policy analysis.
Simulasi Virtual Reality Hemat Biaya untuk Praktikum Kimia Menggunakan Gerakan Tangan Muhammad, David Arizaldi; Husodo, Ario Yudo; Bimantoro, Fitri; Muntari, Muntari
Journal of Computer Science and Informatics Engineering (J-Cosine) Vol 9 No 1 (2025): Juni 2025
Publisher : Informatics Engineering Dept., Faculty of Engineering, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jcosine.v8i1.628

Abstract

We developed a virtual reality (VR) simulation of a chemistry practicum that incorporates the act of pouring liquid using hand gestures. This addresses the need for safe, cost-effective alternatives in chemistry education while still exercising practicum motor skills. Our simulation centers on the practicum of making picric acid, which presents significant both cost and safety challenges. Utilizing Leap Motion hand gesture technology as an input method offers a more affordable option than traditional VR controllers. We conducted usability testing with 16 participants, including a lecturer and undergraduate students from multiple backgrounds, to evaluate the application’s effectiveness and gather feedback. Results indicate that the simulation works as intended and accurately represents the practicum, achieving marginal usability. The application can reduce costs by at least IDR 658,638.00 per session and eliminates hazards associated with handling picric acid, highlighting its potential as a valuable educational tool.
Development of a Convolutional Neural Network Method for Classifying Ripeness Levels of Servo Variety Tomatoes Rosalina, Rosalina; Husodo, Ario Yudo; Wijaya, I Gede Pasek Suta
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.2.4168

Abstract

The distribution of tomatoes in Indonesia is huge, making it an important commodity in the agricultural sector. However, manual classification of tomato ripeness can lead to human error and decrease supply chain efficiency. Therefore, an automated system capable of classifying tomatoes quickly and accurately is needed, in order to reduce the potential for human error and improve supply chain efficiency. This research aims to develop the Convolutional Neural Network (CNN) method to improve the accuracy of tomato ripeness detection through modifications to the architecture, such as reducing several layers, adding batch normalization, and adding dropouts. The dataset used in this study consists of 500 images taken by the researcher himself which are divided into 5 classes, namely unriped, half-riped, riped, half-rotten, and rotten, with each class containing 100 images. There are 3 proposed CNN models, namely the standard model, as well as the addition of batch normalization and dropout in the architecture. The results showed that the proposed model 3 with the addition of dropout on several layers of its architecture is the optimal model with a parameter of 2.4 million and using a batch size of 16 resulting in an accuracy of 98%, as well as precision, recall, and F1-score values of 98%. With these results, the proposed CNN model is effective in identifying the ripeness level of tomato fruit. This research is expected to be applied in the agricultural industry to improve the efficiency of sorting and distributing tomato fruits according to the desired quality standards.
Enhanced Identity Recognition Through the Development of a Convolutional Neural Network Using Indonesian Palmprints Aprilla, Diah Mitha; Husodo, Ario Yudo; Wijaya, I Gede Pasek Suta
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.2.4169

Abstract

The use of palmprint as an identification system has gained significant attention due to its potential in biometric authentication. However, existing models often face challenges related to computational complexity and the ability to scale with larger datasets. This research aims to develop an efficient Convolutional Neural Network (CNN) model for palmprint identity recognition, specifically tailored to address these challenges. A novel contribution of this study is the creation of an original palmprint dataset consisting of 700 images from 50 Indonesian college students, which serves as a foundation for future research in Southeast Asia. The dataset includes different scenarios with varying input sizes (32x32, 64x64, 96x96 pixels) and the number of classes (30, 40, 50) to assess the model's scalability and performance. Three CNN architectures were designed with varying layers, activation functions, and dropout strategies to capture the unique features of palmprints and improve model generalization. The results show that the best-performing model, Model 3, which incorporates dropout layers, achieved 95% accuracy, 96% precision, 95% recall, and 95% F1-score on 50 classes with 1.2 million parameters. Model 1 achieved 98% accuracy, 99% precision, 98% recall, and 98% F1-score on 40 classes with 1.7 million parameters. These findings demonstrate that the proposed CNN models not only achieve high accuracy but also maintain computational efficiency, offering promising solutions for real-time palmprint authentication systems. This research contributes to the advancement of biometric authentication systems, with significant implications for real- world applications in Southeast Asia.
Co-Authors Afwani, Royana Agitha, Nadiyasari Aprilla, Diah Mitha Ardiansyah, Randy Budi Irmawati Darmawan, Riski Dwiyansaputra, Ramaditia Fitri Bimantoro Grendis, Nuraqilla Waidha Bintang Halil Akhyar Hamidi, Mohammad Zaenuddin Hanan, Dimas Afryzal Heri Wijayanto I B K Widiartha I Gede Pasek Suta Wijaya I Wayan Agus Arimbawa, I Wayan Agus Ma'we, Hannatul Muhammad, David Arizaldi Muntari Muntari Murpratiwi, Santi Ika Nugraha, Gibran Satya Rabbani, Budiman Rahayu, Irma Putri Rahayu, Sefani Cahyo Auliya Raihan, Muhammad Dzulhi Rassy, Regania Pasca Rosalina Rosalina Sugianti, Novalia Dwirohmatun Thuy, Tran Thi Thanh Wirarama WW, I Gde Putu