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All Journal Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Ilmiah Edutic : Pendidikan dan Informatika DEVICE : JOURNAL OF INFORMATION SYSTEM, COMPUTER SCIENCE AND INFORMATION TECHNOLOGY Jurnal Manajemen Informatika dan Bisnis Digital J-ENSITEC (Journal of Engineering and Sustainable Technology) SISFOTENIKA Akademika Media Elektrik
Alghifari, Hamzah
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Aerospace Engineering Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering Neuroscience Transportation

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Klasifikasi Risiko Gempa Bumi menggunakan metode Decision Tree Akbar, Niko; Alghifari, Hamzah; Abdillah, Nurul; Dahwanu, Oki
DEVICE : JOURNAL OF INFORMATION SYSTEM, COMPUTER SCIENCE AND INFORMATION TECHNOLOGY Vol 6, No 2: DESEMBER 2025
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/device.v6i2.7843

Abstract

Gempa bumi merupakan salah satu bencana alam yang sulit diprediksi namun memiliki dampak besar terhadap kehidupan manusia. Oleh karena itu, diperlukan suatu pendekatan analitis yang mampu mengidentifikasi pola dan hubungan antarparameter gempa untuk mendukung sistem peringatan dini. Penelitian ini bertujuan untuk melakukan Klasifikasi Dampak akan kejadian gempa bumi menggunakan metode Data Mining berbasis Decision Tree. Data yang digunakan berasal dari katalog gempa yang memuat atribut seperti Tanggal Kejadian (timestamp), magnitudo, kedalaman, serta koordinat lokasi (latitude dan longitude). Proses analisis meliputi tahap pembersihan data (data cleaning), transformasi, dan pembuatan model klasifikasi Decision Tree untuk menentukan tingkat potensi dan dampak gempa serta mengetahui keakuratan gempa bumi berdasarkan data dari tahun ketahun. Hasil penelitian menunjukkan bahwa atribut magnitudo memiliki pengaruh signifikan terhadap tingkat risiko gempa. Model Decision Tree yang dibangun mampu menghasilkan aturan klasifikasi seperti “Jika magnitudo <5 maka berpotensi Risiko gempa bumi rendah, sedangkan magnitudo antara 5-7 berisiko gempa bumi sedang, dan magnitudo ≥ 7 maka berpotensi Risiko Gempa Bumi Tinggi”, yang dapat digunakan untuk mendukung pengambilan keputusan dalam mitigasi bencana. Dengan demikian, metode Decision Tree terbukti efektif dalam mengungkap pola tersembunyi dari data gempa bumi dan dapat menjadi dasar bagi sistem prediksi serta peringatan dini gempa di masa mendatang. Disimpulkan bahwa akurasi masing – masing sebesar 100 %, sedangkan recall sebesar 100 % tapi hasil precision menunjukkan statistik yang berbeda yakni Prediksi Tidak Berpotensi Gempa Bumi  Besar sebesar 100%, sedangkan Prediksi Tidak berpotensi Gempa Bumi sedang sebesar 99,67%, dan terakhir prediksi Tidak berpotensi Gempa Bumi Kecil sebesar 96 %. Hasil pengujian ternyata menghasilkan magnitude rendah dengan ukuran >5,350 tergolong rendah, sedangkan magnitude rendah dengan ukuran <5,350 mempunyai frekuensi yang banyak. Dan Beberapa data berdasarkan statistik Magnitudo >5.350 ukuran sedang dari data sebanyak 16 data. Sedangkan magnitudo ≤ 5.350 Ukuran Rendah sebanyak 6716 Data yang ditemukan dan sudah dianalisis.
ANALISIS DAN PERANCANGAN SISTEM INFORMASI PEMASARAN PERUMAHAN BERBASIS WEB MENGGUNAKAN METODE PROTOTYPE PADA PT LESTARI INTI PROPERTI JAMBI: Marketing Information System, Web-Based Application, Prototype Method, Unified Modeling Language, Subsidized Housing Alghifari, Hamzah; Akbar, Niko; Abdillah, Nurul; Dahwanu, Oki
JURNAL AKADEMIKA Vol 18 No 1 (2025): Jurnal Akademika
Publisher : LP2M Universitas Nurdin Hamzah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53564/6psph742

Abstract

Due to its manual reliance on conventional promotional techniques such as distributing flyers and posters, PT Lestari Inti Properti faces challenges in selling subsidized housing products. In the digital era, this situation limits promotional reach and reduces marketing effectiveness. The goal of this project is to create a web-based marketing information system that can manage the company's sales and administration data in an integrated manner and function as a digital promotional tool. Using the Unified Modeling Language (UML) methodology, prototyping techniques are applied through the phases of rapid design, testing and feedback, prototyping, and requirements communication. The result of this study is the design of a prototype housing marketing information system that includes a login module, housing data management, ordering procedures, and transaction reports. Use case structures, activity diagrams, and class diagrams are used in the design of this system to fully explain how users interact with business operations. This system can increase marketing reach, speed up administrative procedures, and reduce data input errors, according to the design results. Therefore, PT Lestari Inti Properti can improve operational effectiveness and competitiveness in the digital market by using a web-based marketing information system.
Banana Ripeness Classification Using Convolutional Neural Network Based on Resnet-50 Khalis Fikri, Muhammad; 'Asyarina Ramadhani, Salisa; Haryus Wirasapta, Andicho; Rabiula, Andre; Anzari, Yandi; Alghifari, Hamzah; Sajjad Mishi, Salmuna
Jurnal Media Elektrik Vol. 23 No. 2 (2026): MEDIA ELEKTRIK
Publisher : Jurusan Pendidikan Teknik Elektro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/metrik.v23i2.11482

Abstract

The manual assessment of banana ripeness on an industrial scale is subjective, time-consuming, and inconsistent. This necessitates an automated computer vision system. Previous studies have used shallow Convolutional Neural Networks (CNNs) for binary classification, but these networks often struggle with complex ripening stages and degrade in deeper networks. This study addresses this gap by implementing a deep learning algorithm using the ResNet-50 architecture. The residual block mechanism extracts fine-grained visual features without vanishing gradient issues. The model was evaluated using a diverse dataset of 13,478 digital images spanning four stages of banana ripeness: overripe, ripe, rotten, and unripe. Using a 95-5 train and test-validation split, the model was optimized over 50 epochs with a categorical cross-entropy loss function. The proposed model achieved outstanding accuracy (98.13%), minimal loss (0.1237), and average precision, recall, and F1-scores of 98.09%, 98.20%, and 98.14%, respectively. This study scientifically validates the robustness of deep residual networks in complex agro-industrial pattern recognition. Furthermore, with an inference time of approximately 50 ms per image, the system is ready for seamless integration into an automated sorting line.
Development of a Web-Based Application for Predicting Stroke Patient Emergency Levels Using the Naïve Bayes Algorithm Nurul Abdillah; Oki Dahwanu; Hamzah Alghifari; Niko Akbar
EDUTIC Vol 13, No 1: 2026
Publisher : Universitas Trunodjoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/edutic.v13i1.34303

Abstract

Stroke is a serious disease that requires prompt and appropriate treatment; therefore, determining the level of patient Emenrgency Levels is critically important. This study aims to develop a web-based application for predicting the Emenrgency Levels level of stroke patients using the Naïve Bayes algorithm as a classification method. The research data were obtained from the medical records of stroke patients at RSUP Dr. M. Djamil Padang during March and April 2025, with a total of 222 data samples. The attributes used in this study include age, gender, address, length of stay, ward class, BPJS insurance membership status, and comorbidities, with Emenrgency Levels status as the class attribute classified into Emenrgency and non-Emenrgency. The application was developed as a web-based system to facilitate easy access for medical personnel in utilizing the prediction system. The experimental results indicate that the Naïve Bayes algorithm achieved an accuracy of 77.48% with an error rate of 22.52%. The findings of this study are expected to assist medical personnel in supporting faster and more objective decision-making regarding the Emenrgency Levels level of stroke patients.
Perancangan dan Evaluasi UI/UX Sistem Manajemen Magang pada Kantor Pertanahan Jambi Menggunakan Metode Design Thinking Azral Ahmad Rajasa; Hamzah Alghifari; Edi Saputra; Benedika Ferdinan Hutabarat; Raihan Irawan

Publisher :

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v8i6.10004

Abstract

Abstrak - Penelitian ini membahas perancangan User Interface (UI) dan User Experience (UX) pada website Sistem Informasi Manajemen Magang (SIMAGANG) di Kantor Pertanahan Kota Jambi yang bertujuan untuk mengatasi permasalahan administrasi magang yang masih dilakukan secara manual. Metode yang digunakan adalah design thinking dengan lima tahapan utama yaitu empathize, define, ideate, prototype, dan test. Data dikumpulkan melalui observasi, wawancara, dan studi literatur guna memahami kebutuhan pengguna, yang terdiri dari admin arsip dan peserta magang. Hasil perancangan menghasilkan prototype sistem dengan fitur utama seperti pendaftaran online, presensi digital berbasis kamera, logbook harian, serta dashboard penilaian magang. Pengujian usability dilakukan menggunakan platform Maze dan instrumen System Usability Scale (SUS). Hasil pengujian menunjukkan skor Maze Usability Score (MAUS) sebesar 98 (kategori tinggi) dan skor SUS sebesar 100 (kategori excellent usability). Nilai ini menunjukkan bahwa desain UI/UX SIMAGANG memiliki tingkat kebergunaan yang sangat baik, mudah dipahami, dan efisien digunakan. Kesimpulannya, penerapan metode design thinking berhasil menghasilkan desain antarmuka yang berorientasi pada pengguna dan mampu meningkatkan efisiensi pengelolaan administrasi magang di Kantor Pertanahan Kota Jambi.Kata kunci : Perancangan UI/UX; Sistem Informasi Magang; Design Thinking; Usability; Kantor Pertanahan; Abstract - This study discusses the design of the User Interface (UI) and User Experience (UX) for the Internship Management Information System (SIMAGANG) website at the Land Office of Jambi City, which aims to address administrative internship processes that are still conducted manually. The research employed the design thinking method consisting of five main stages: empathize, define, ideate, prototype, and test. Data were collected through observation, interviews, and literature studies to identify user needs involving administrative staff and internship participants. The design process produced a system prototype featuring online registration, camera-based digital attendance, a digital logbook, and an internship evaluation dashboard. Usability testing was conducted using the Maze platform and the System Usability Scale (SUS) instrument. The results showed a Maze Usability Score (MAUS) of 98 (high category) and a System Usability Scale (SUS) score of 100 (excellent usability category), indicating that the SIMAGANG UI/UX design achieved a high level of usability, ease of use, and efficiency. In conclusion, the application of the design thinking method successfully produced a user-centered interface design that improves the efficiency of internship administration management at the Land Office of Jambi City.Keywords: UI/UX Design; Internship Information System; Design Thinking; Usability; National Land Agency;
Rancang Bangun Sistem Informasi Manajemen Magang Berbasis Website dengan Metode Rapid Application Development (RAD) Raihan Irawan; Hamzah Alghifari; Edi Saputra; Benedika Ferdinan Hutabarat; Azral Ahmad Rajasa
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 8, No 6 (2025): Desember 2025
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v8i6.10005

Abstract

Abstrak - Kantor Pertanahan Kota Jambi menghadapi tantangan dalam pengelolaan kegiatan magang karena sistem administrasi yang masih dilakukan secara manual, sehingga menghambat efisiensi dan akurasi dalam pencatatan data peserta. Untuk mengatasi hal tersebut, penelitian ini merancang Sistem Informasi Manajemen Magang berbasis website yang berfungsi sebagai sarana digital untuk mendukung proses administrasi dan pemantauan kegiatan magang. Sistem ini dikembangkan menggunakan metode Rapid Application Development (RAD) yang berfokus pada kecepatan dan ketepatan dalam pengembangan melalui tahapan requirements planning, user design, construction, dan cutover. Pengujian dilakukan menggunakan metode Black Box Testing untuk memastikan fungsi sistem berjalan sesuai spesifikasi, serta User Acceptance Testing (UAT) untuk menilai tingkat penerimaan pengguna. Hasil penelitian menunjukkan bahwa sistem mampu meningkatkan efisiensi administrasi, mempercepat proses absensi dan penilaian, serta meminimalkan kesalahan manual. Dengan demikian, sistem ini menjadi solusi efektif dalam mendukung digitalisasi pengelolaan magang di Kantor Pertanahan Kota Jambi.Kata kunci : Sistem Informasi; Magang; Website; Rapid Application Development (RAD); Kantor Pertanahan Kota Jambi; Abstract - The Jambi City Land Office faces challenges in managing internship activities due to a manual administrative system, which hinders efficiency and accuracy in recording participant data. To address this issue, this study designed a web-based Internship Management Information System that serves as a digital platform to support administrative processes and internship monitoring. The system was developed using the Rapid Application Development (RAD) method, which focuses on speed and accuracy through the stages of requirements planning, user design, construction, and cutover. Testing was carried out using the Black Box Testing method to ensure that each system function operated according to specifications, as well as User Acceptance Testing (UAT) to assess user acceptance levels. The results show that the system successfully improves administrative efficiency, accelerates attendance and assessment processes, and minimizes manual errors. Therefore, this system provides an effective solution to support the digitalization of internship management at the Jambi City Land Office.Keywords: Information System; Internship; Website; Rapid Application Development (RAD); Jambi City Land Office;
Comparison of Machine Learning Algorithms (SVM, Random Forest, and Naïve Bayes) for Predicting Rice Production Oki Dahwanu; Nurul Abdillah; Niko Akbar; Hamzah Alghifari
J-ENSITEC (Journal of Engineering and Sustainable Technology) Vol. 12 No. 02 (2026): June 2026
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/j-ensitec.v12i02.18386

Abstract

Global rice production faces mounting pressure from population growth and climate change, yet traditional statistical models fail to capture the complex nonlinear dynamics between environmental factors and crop yields. To address this gap, this study systematically compares the accuracy of three machine learning algorithms, Support Vector Machine (SVM), Random Forest (RF), and Naïve Bayes (NB) for predicting rice production fluctuations due to climate change using the latest local climate data from Indonesia. A dataset of 96 monthly observations (2018–2025) comprising climate features (temperature, humidity, wind speed, precipitation, cloud cover, sunshine duration) and rice production categories (Low, Medium, High) was analyzed. Algorithm performance was evaluated using accuracy, precision, recall, and F1-score. The results demonstrate that Random Forest significantly outperforms the other methods, achieving an accuracy of 95%, precision of 0.9571, recall of 0.95, and F1-score of 0.95, compared to SVM (75% accuracy) and Naïve Bayes (70% accuracy). This study provides the first head-to-head comparison of these three algorithms for rice yield prediction in Indonesia using current climate data. The key benefit over pre-existing approaches is the empirical confirmation that ensemble learning, particularly Random Forest, offers superior predictive reliability for crop yield forecasting under high feature complexity, thereby enabling more accurate, data-driven agricultural policy and food security planning.
KLASIFIKASI DAN PREDIKSI KELUARGA BERISIKO STUNTING DI PROVINSI JAMBI MENGGUNAKAN METODE KNN DAN NAIVE BAYES Niko Akbar; Hamzah Alghifari; Nurul Abdillah; Oki Dahwanu
DEVICE : JOURNAL OF INFORMATION SYSTEM, COMPUTER SCIENCE AND INFORMATION TECHNOLOGY Vol 7, No 1: JUNI 2026
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/device.v7i1.8522

Abstract

Di Indonesia Prevalensi stunting sebesar 37,2%, naik dari 35,6% pada tahun 2019 dan 36,8%, dengan mayoritas dipengaruhi oleh penduduk setempat. Kementerian Kesehatan Indonesia memperkirakan bahwa prevalensi stunting akan mencapai 38,9% pada tahun 2020. Permasalahannya Beberapa Data yang diambil dan dipakai berupa data sekunder yang di ada diwebsite opendata provinsi jambi yang berjudul Faktor Penapisan Keluarga Berisiko Stunting di Provinsi Jambi. Dengan menggunakan algoritma KNN dan Naïve Bayes, maka didapatkan hasilnya berupa kedua algoritma cocok untuk pengklasteran dan Scoring dari algoritma menunjukkan hasil yang berbeda. Karena keakurasiannya sesuai dengan perhitungan manual yang telah dijabarkan pada bagian pengolahan data. Beberapa hasil dari cross-validasi menyatakan bahwa nilai accuracy 85,29%, nilai precision berupa 83,33%, nilai recall 85,29%.
Komparasi Efektivitas Augmented Reality dan Virtual Reality sebagai Media Pembelajaran: Tinjauan Sistematis: Comparative Effectiveness of Augmented and Virtual Reality Learning: Systematic Review Hamzah Alghifari; Niko Akbar; Nurul Abdillah; Oki Dahwanu
SISFOTENIKA Vol. 16 No. 2 (2026): SISFOTENIKA
Publisher : STMIK PONTIANAK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30700/sisfotenika.v16i2.643

Abstract

Teknologi imersif seperti Augmented Reality (AR) dan Virtual Reality (VR) telah menciptakan peluang baru dalam bidang pendidikan. Namun, masih ada perdebatan tentang teknologi mana yang dapat meningkatkan hasil pembelajaran. Tujuan dari penelitian ini adalah untuk membandingkan efektivitas realitas maya (AR) dan realitas virtual (VR) sebagai media pembelajaran dengan menggunakan pendekatan Systematic Literature Review (SLR) yang didasarkan pada PRISMA 2020. Tiga basis data utama (Scopus, IEEE Xplore, dan ScienceDirect) digunakan untuk melakukan pencarian literatur dari tahun 2019 hingga tahun 2020. Dari 562 artikel yang ditemukan, 42 memenuhi persyaratan inklusi dan dianalisis secara naratif-komparatif. Hasil penelitian menunjukkan bahwa dibandingkan dengan metode konvensional, kedua teknologi meningkatkan motivasi, keterlibatan, dan hasil belajar. Sementara realitas virtual memiliki keunggulan dalam simulasi skenario berbahaya, retensi materi kompleks, dan imersi mendalam, AR unggul dalam aksesibilitas, kemudahan adopsi, dan integrasi konteks dunia nyata. Bahasa, STEM, dan kedokteran adalah bidang yang paling banyak memanfaatkan keduanya. Biaya perangkat, kesiapan guru, dan infrastruktur adalah masalah utama, terutama di negara berkembang. Studi ini membantu memilih teknologi berdasarkan tujuan pembelajaran dan konteks institusi. Kata kunci—Augmented reality, Virtual reality, Efektivitas Pembelajaran, Media Pembelajaran, Systematic Literature Review.
Systematic Literature Review: Integrasi Explainable AI dalam Decision Support System untuk Manajemen Risiko dan Krisis Muhammad Damas Fatih; Hamzah Alghifari; Pariyadi Pariyadi; Mohammad Alfiza Rayesa
JUMINTAL: Jurnal Manajemen Informatika dan Bisnis Digital Vol. 5 No. 1 (2026): Mei 2026
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jumintal.v5i1.8113

Abstract

The growing complexity of risks and crisis situations across various sectors has encouraged the development of Artificial Intelligence-based Decision Support Systems (DSS) to support more accurate, responsive, and data-driven decision-making. These systems are considered valuable because they can help decision-makers analyze uncertainty, recognize potential threats, predict possible impacts, and determine appropriate actions in complex conditions. However, the use of black-box AI models creates serious challenges, particularly regarding transparency, accountability, interpretability, and user trust. Therefore, this study focuses on examining the trend of Explainable Artificial Intelligence (XAI) integration in DSS for risk and crisis management, identifying the XAI methods commonly applied, and revealing research gaps that still need further attention. The study applies a Systematic Literature Review (SLR) method using the PRISMA approach, involving 47 selected articles obtained from Scopus, IEEE Xplore, ScienceDirect, and Google Scholar databases, published between 2020 and 2025. The findings show that XAI plays an important role in improving transparency, interpretability, and trust in AI-based DSS, with SHAP and LIME being the most frequently used methods. Nevertheless, gaps remain, especially limited XAI implementation in real-time crisis scenarios and insufficient human-centered design approaches. This study contributes a conceptual framework integrating DSS, XAI, and risk management.
Co-Authors Andre Rabiula Azral Ahmad Rajasa Benedika Ferdinan Hutabarat Edi Saputra Khalis Fikri, Muhammad Mohammad Alfiza Rayesa Muhammad Damas Fatih Niko Akbar Niko Akbar Niko Akbar Nurul Abdillah Nurul Abdillah Oki Dahwanu Pariyadi Pariyadi Raihan Irawan Ramadhani, Salisa 'Asyarina Sajjad Mishi, Salmuna Wirasapta , Andicho Haryus Yandi Anzari