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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal sistem informasi, Teknologi informasi dan komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JUTIK : Jurnal Teknologi Informasi dan Komputer GERVASI: Jurnal Pengabdian kepada Masyarakat Jurnal Kridatama Sains dan Teknologi To Maega: Jurnal Pengabdian Masyarakat Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Journal of Computer Networks, Architecture and High Performance Computing J-Eltrik Journal of Technology and Informatics (JoTI) Jurnal Info Sains : Informatika dan Sains TIERS Information Technology Journal Jurnal Ilmiah Telsinas Elektro, Sipil dan Teknik Informasi Journal of Language and Health Indonesian Journal of Multidisciplinary on Social and Technology
Gede Humaswara Prathama
Universitas Pendidikan Nasional
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Nursing Public Health Social Sciences Other

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User-Centered Design Approach in Developing User Interface and User Experience of Sculptify Mobile Application Dananjaya, Md. Wira Putra; Prathama, Gede Humaswara; Darmaastawan, Kadek
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 3 (2024): Articles Research Volume 6 Issue 3, July 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i3.4206

Abstract

In the increasingly digital era, user interface (UI) and user experience (UX) design have become crucial factors in application development. The success of an application is not only determined by its functionality, but also by how well users can interact with the application. User Centered Design (UCD) is an approach that places users as the main focus in every stage of design, from initial research to final evaluation, to ensure that the resulting product truly meets user needs and expectations. This study applies the UCD approach to the UI and UX design of the Sculptify application, which is designed to facilitate the buying and selling of sculptures and other three-dimensional works of art. Given the complexity and uniqueness of art product transactions, effective UI and UX design is very important. This study involves the active participation of potential users through methods such as interviews, surveys, and usability testing to create an intuitive interface and provide a satisfying experience for users. The research stage begins with research to understand user needs and preferences, followed by initial design and a series of tests and iterations based on user feedback. The final evaluation is carried out to measure the extent to which the final design meets user needs and expectations. The results of the UCD implementation are expected to provide valuable insights into the importance of placing users at the center of the design process and how this can improve the quality of interactions and overall user satisfaction.
Analisis Determinan Karakter Siswa Menggunakan Explainable Machine Learning (SHAP) dan Klasterisasi Profil Sekolah Studi Kasus Rapor Pendidikan Provinsi Bali Dananjaya, Md. Wira Putra; Krisnawijaya, Ngakan Nyoman Kutha; Prathama, Gede Humaswara; Paramartha, I Gusti Ngurah Darma; Gama, Adie Wahyudi Oktavia
Jurnal Kridatama Sains dan Teknologi Vol 7 No 02 (2025): Jurnal Kridatama Sains dan Teknologi
Publisher : Universitas Ma'arif Nahdlatul Ulama Kebumen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53863/kst.v7i02.1988

Abstract

Strengthening student character is a key performance indicator in the Merdeka Belajar curriculum, but the identification of the school environment's most influential determinants of character achievement is often assumed. This study aims to quantitatively deconstruct the relationship between school climate and student character quality in Bali Province. Using the Indonesian Education Report dataset released by the Ministry of Primary and Secondary Education (Kemendikdasmen) for the 2023-2025 period with a total of 727 data entries, this study applies the Educational Data Mining methodology with the Random Forest algorithm enhanced by the Synthetic Minority Over-sampling Technique (SMOTE) to address data inequality. The novelty of this study lies in the use of SHapley Additive exPlanations (SHAP) for model transparency and K-Means Clustering for zoning mapping. Experimental results show the model is able to predict character achievement with 77.03% accuracy. The SHAP analysis revealed the interesting finding that Climate for Diversity (influence score of 0.45) and Climate for Gender Equality (0.22) were the strongest predictors, far exceeding the influence of Climate for Security (0.13). This finding challenges the common assumption that physical security is the single most important factor. Furthermore, the clustering analysis identified three school typologies in Bali, including one "Vulnerable" cluster that scored critically on gender equality and diversity despite having adequate security scores. This study recommends shifting the focus of education policy in Bali from a physical security approach to strengthening tolerance and gender equality programs, which have been shown to have a more statistically significant impact
Klasifikasi Kualitas Tanah Berdasarkan Kandungan pH, Kelembapan, dan Suhu Menggunakan Algoritma K-Nearest Neighbors Md Wira Putra Dananjaya; Gede Humaswara Prathama; I Gusti Ngurah Darma Paramartha; Putu Gita Pujayanti
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 9 No 4 (2025): OCTOBER-DECEMBER 2025
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v9i4.4049

Abstract

This study aims to analyze soil quality using the K-Nearest Neighbors (KNN) algorithm based on environmental parameters such as temperature, humidity, pH, and nutrient content (N, P, K). The dataset used consists of 660 entries covering 22 different classes describing soil types with varying characteristics. The KNN model was applied to classify soil quality, and the results were evaluated using the Confusion Matrix and Classification Report. The accuracy of the model obtained was around 61%, indicating potential improvements in the classification of some more difficult soil classes. The model performed better on certain classes such as kidney beans, chickpeas, and grapes, but was less than optimal on other classes such as watermelon and pomegranate. These results indicate class alignment in the dataset that affects model performance. This study contributes to the application of machine learning algorithms in agriculture, especially for soil quality monitoring. In the future, this study opens up opportunities for further improvements by using parameter optimization techniques and other more complex algorithms. Thus, the results of this study can be used as a basis for developing intelligent systems for more effective and efficient soil management.
Comparative Performance of Machine Learning Algorithms for Diabetes Prediction I Made Ardi Sudestra; Adie Wahyudi Oktavia Gama; Gede Humaswara Prathama; I Gusti Ngurah Darma Paramartha; Musawer Hakimi
Journal of Technology and Informatics (JoTI) Vol. 8 No. 1 (2026): Vol. 8 N. 1 (2026)
Publisher : Universitas Dinamika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37802/joti.v8i1.1195

Abstract

Early detection of diabetes mellitus is crucial to prevent severe complications. This study evaluates three machine learning algorithms for diabetes prediction using a quantitative comparative experimental design. The algorithms are k-Nearest Neighbors (k-NN), Support Vector Machine (SVM), and Random Forest. These methods were chosen to compare distinct learning paradigms. k-NN is distance-based, SVM is margin-based, and Random Forest is an ensemble method. The goal is to find the optimal model for clinical use. The Pima Indians Diabetes dataset was used. It includes 390 patients and 15 clinical features. Performance was measured by accuracy, precision, recall, and F1-score. Random Forest had the highest accuracy (89.7%) and F1-score, providing the most balanced classification. SVM followed with 84.6%, and k-NN achieved 76.9%. Although k-NN had the highest recall (0.750), its precision was low (0.375), showing a high false-positive rate. Feature importance analysis pointed to blood glucose levels as the most significant predictor, which matches clinical knowledge. In summary, ensemble techniques like Random Forest offer the most reliable results. This highlights the importance of selecting the right algorithm for early diabetes detection in clinical applications.
Mitigasi Double Booking pada Penjadwalan Dermaga Melalui Sistem Visualisasi Spasial Real-Time Dimas Rangga Marshandika; Gede Humaswara Prathama; Adie Wahyudi Oktavia Gama; Md. Wira Putra Dananjaya
Indonesian Journal of Multidisciplinary on Social and Technology Vol. 4 No. 3 (2026): Juli - Oktober
Publisher : PT Ilmu Data Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69693/ijmst.v4i3.13111

Abstract

Pelabuhan Benoa sebagai salah satu simpul penting logistik maritim menghadapi tantangan operasional berupa latensi informasi dan potensi tumpang tindih jadwal (double booking) pada proses pra-penjadwalan sandar kapal yang masih dikelola secara konvensional. Penelitian ini bertujuan untuk merancang dan membangun sebuah sistem Pra-Booking dan monitoring dermaga berbasis website yang dilengkapi dengan visualisasi Berthing Plan secara real-time. Pembangunan perangkat lunak ini menggunakan metode Prototyping dengan memanfaatkan kerangka kerja React.js berarsitektur Virtual DOM pada sisi antarmuka untuk merender tata letak grafik dua dimensi secara responsif. Pada sisi server, sistem menggunakan Node.js dan pustaka Socket.io untuk mengelola transmisi data berlatensi rendah. Basis data PostgreSQL digunakan untuk mengamankan integritas transaksional melalui penerapan mekanisme penguncian baris data (row-level locking). Logika pencegahan benturan jadwal dikelola melalui algoritma Bounding Box Collision yang mengevaluasi irisan spasial dan temporal secara simultan, serta didukung oleh fitur mitigasi keterlambatan operasional (Extend Time). Hasil pengujian fungsional menggunakan metode Black Box Testing yang menerapkan teknik Equivalence Partitioning dan Boundary Value Analysis menunjukkan tingkat keberhasilan 100%. Hal ini membuktikan bahwa algoritma validasi kapasitas fisik dermaga dan deteksi konflik beroperasi secara akurat. Selanjutnya, pengujian penerimaan pengguna (User Acceptance Testing) yang melibatkan agen pelayaran menghasilkan persentase rata-rata sebesar 90,63%, menempatkan sistem pada kategori "Sangat Layak". Sistem ini terbukti efektif mereduksi miskomunikasi, menjaga transparansi data, dan siap diimplementasikan untuk mendukung tata kelola operasional di Dermaga Timur Pelabuhan Benoa.
Pemodelan Prediksi Banjir di Kota Denpasar Menggunakan Random Forest Theresia Ananda Eleonora; Adie Wahyudi Oktavia Gama; Gede Humaswara Prathama; Md. Wira Putra Dananjaya
Indonesian Journal of Multidisciplinary on Social and Technology Vol. 4 No. 3 (2026): Juli - Oktober
Publisher : PT Ilmu Data Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69693/ijmst.v4i3.13424

Abstract

Banjir merupakan salah satu bencana hidrometeorologi yang paling sering terjadi di Indonesia, dan Kota Denpasar, Bali, menghadapi kejadian banjir berulang akibat pesatnya urbanisasi, berkurangnya daerah resapan air, serta kondisi topografi yang rendah. Penelitian ini mengembangkan model prediksi potensi banjir untuk Kota Denpasar menggunakan algoritma Random Forest, yang dilatih menggunakan lima variabel lingkungan berbasis penginderaan jauh yang diekstraksi melalui Google Earth Engine, yaitu curah hujan (CHIRPS), elevasi (SRTM), tutupan lahan (ESA WorldCover), indeks vegetasi (NDVI dari Landsat 8), dan kelembapan tanah (ERA5-Land). Data historis lokasi kejadian banjir dari Badan Penanggulangan Bencana Daerah (BPBD) Kota Denpasar periode 2020-2025 digabungkan dengan variabel-variabel lingkungan tersebut untuk membentuk dataset berlabel. Teknik Random Undersampling diterapkan untuk mengatasi ketidakseimbangan kelas, menghasilkan dataset seimbang sebanyak 416 data, yang kemudian dibagi menjadi 80% data latih dan 20% data uji. Tiga konfigurasi model dibangun dan dibandingkan, yaitu Random Forest baseline, model hasil optimasi Grid Search, dan model hasil optimasi Particle Swarm Optimization (PSO), yang masing-masing dievaluasi menggunakan metrik accuracy, precision, recall, dan F1-score. Di antara ketiga konfigurasi model, model Random Forest baseline menghasilkan performa terbaik secara keseluruhan, dengan accuracy tertinggi (88,10%), precision tertinggi (83,33%), recall tertinggi (95,24%), dan F1-score tertinggi (88,89%), serta waktu komputasi tersingkat (0,23 detik). Sebaliknya, optimasi menggunakan Grid Search dan PSO justru menurunkan performa di seluruh metrik evaluasi dibandingkan baseline, dengan accuracy masing-masing sebesar 84,52% dan 85,71%. Temuan ini menunjukkan bahwa kombinasi data penginderaan jauh dengan algoritma Random Forest mampu menghasilkan model prediksi potensi banjir yang andal untuk mendukung perencanaan mitigasi bencana di Kota Denpasar.
Co-Authors Adie Wahyudi Oktavia Gama Anggit Septio Budi Ariesta, Ni Luh Wina Sinta Dananjaya, Md Wira Putra Dananjaya, Md. Wira Putra Darmaastawan, Kadek Devi Anggreni, Ni Komang Ayu Dewa Ayu Putu Adhiya Garini Putri Dewiningrat, Ayu Indira Dhipa Andaresta Dimas Rangga Marshandika Grren, Agustini Degni Melsy Gusi Putu Lestara Permana I Gusti Ngurah Darma Paramartha I Made Ardi Sudestra I Made Pinka Dwipayana I Made Sindhu Yoga I Nyoman Sunarta I Wayan Aditya Suranata I Wayan Sukadana I Wayan Sukadana Kadek Adyatna Wedananta Kadek Darmaastawan Kadek Linda Kusnita M Febrian Eibim Adzariatulah Md. Wira Putra Dananjaya Musawer Hakimi Ngakan Nyoman Kutha Krisnawijaya Ni Made Ary Esta Dewi Wirastuti Permana, Putu Indra Putu Gita Pujayanti Putu Purnama Dewi Sudestra, I Made Ardi Theresia Ananda Eleonora Widnyani, Ni Made Yoga Divayana