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All Journal MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Abdi Insani Journal of Computer and Information System (J-CIS) SainsTech Innovation Journal Jurnal Ilmiah Edutic : Pendidikan dan Informatika IJMST SainsTech Innovation Journal
Ahmad Fatoni Dwi Putra
Universitas Qamarul Huda Badaruddin Bagu
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics

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Optimizing Rain Prediction Model Using Random Forest and Grid Search Cross-Validation for Agriculture Sector Ahmad Fatoni Dwi Putra; Muhamad Nizam Azmi; Heri Wijayanto; Satria Utama; I Gede Putu Wirarama Wedashwara Wirawan
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 23 No 3 (2024)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i3.3891

Abstract

Agriculture, as a sector that is highly influenced by weather conditions, faces challenges due to increasingly unpredictable changes in weather patterns. The aim of this research is to create an optimal rainfall prediction model to help farmers create irrigation schedules, use fertilizer, and planting schedules, and protect plants from extreme weather events. The method used in this research to obtain the best rain prediction model is to use the random forest algorithm and the grid search cross-validation algorithm. Random Forest, known for its robustness and accuracy, emerged as a suitable algorithm for predicting rain. utilizing a substantial dataset from the West Nusa Tenggara Meteorology, Climatology, and Geophysics Agency covering the period 2000 to 2023. The data is then processed first to ensure its readiness for use. This process involves removing outlier data points, empty data entries, and unused features. After the preprocessing stage, the data underwent training using the Random Forest algorithm, resulting in an R-squared value of 0.1334. To obtain the optimal model, Grid Search Cross Validation is used. The results of this research obtained the best rain prediction model with an R-squared value of 0.0268. This model will be used to predict rain in the agricultural sector. This research concludes that we can get the best rain prediction model by combining Random Forest and Gird Search Cross-Validation. For further research, we can compare other rain prediction methods, add features, and combine datasets from a wider area.
Optimizing Rain Prediction Model Using Random Forest and Grid Search Cross-Validation for Agriculture Sector Ahmad Fatoni Dwi Putra; Muhamad Nizam Azmi; Heri Wijayanto; Satria Utama; I Gede Putu Wirarama Wedashwara Wirawan
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 3 (2024)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i3.3891

Abstract

Agriculture, as a sector that is highly influenced by weather conditions, faces challenges due to increasingly unpredictable changes in weather patterns. The aim of this research is to create an optimal rainfall prediction model to help farmers create irrigation schedules, use fertilizer, and planting schedules, and protect plants from extreme weather events. The method used in this research to obtain the best rain prediction model is to use the random forest algorithm and the grid search cross-validation algorithm. Random Forest, known for its robustness and accuracy, emerged as a suitable algorithm for predicting rain. utilizing a substantial dataset from the West Nusa Tenggara Meteorology, Climatology, and Geophysics Agency covering the period 2000 to 2023. The data is then processed first to ensure its readiness for use. This process involves removing outlier data points, empty data entries, and unused features. After the preprocessing stage, the data underwent training using the Random Forest algorithm, resulting in an R-squared value of 0.1334. To obtain the optimal model, Grid Search Cross Validation is used. The results of this research obtained the best rain prediction model with an R-squared value of 0.0268. This model will be used to predict rain in the agricultural sector. This research concludes that we can get the best rain prediction model by combining Random Forest and Gird Search Cross-Validation. For further research, we can compare other rain prediction methods, add features, and combine datasets from a wider area.
PELATIHAN DESAIN GRAFIS SEBAGAI UPAYA PENINGKATAN PENGETAHUAN DAN KETERAMPILAN DALAM PEMASARAN KONTEN SEBAGAI PELUANG MENDAPATKAN PASSIVE INCOME BAGI KARANG TARUNA CIPTA RASA DAYA DI DESA KARANG SIDEMEN Syuhada, Fahmi; Saputra, Joni; Adipta, Marazaenal; Anggarista, Randa; Kumoro, Danang Tejo; Afriansyah, M.; Lonang, Syahrani; Putra, Ahmad Fatoni Dwi; Firdaus, Asno Azzawagama; Pratama, Ramadhana Agung; Yamin, Muhamad
Jurnal Abdi Insani Vol 12 No 5 (2025): Jurnal Abdi Insani
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/abdiinsani.v12i5.2235

Abstract

The Community Partnership Empowerment activity aimed to enhance the skills and knowledge of the youth in Karang Sidemen Village, Central Lombok, in the field of digital creative economy, specifically through digital content marketing that can generate passive income. The PKM program is supported by the Directorate of Research, Technology, and Community Service through the BIMA 2024 program. The activities included a socialization session on the concept of the creative economy and technical training on using Adobe Illustrator, where participants were encouraged to market their creations on platforms like Shutterstock. The outcomes of this program showed an improvement in participants' graphic design skills, as evidenced by their ability to create logos, set up Shutterstock accounts, and independently upload their work. Additionally, this activity involved students under the Merdeka Belajar-Kampus Merdeka (MBKM) scheme, providing them with experiential learning outside the campus. In conclusion, this program successfully made a positive impact on digital literacy and the creative economy in the community and is expected to contribute to the village's economic sustainability through the empowerment of local potential in a sustainable manner.
PENGEMBANGAN MEDIA PEMBELAJARAN GAME MENYUSUN PUZZLE PAHLAWAN INDONESIA BERBASIS JAVASCRIPT Satrio Budi Santoso; Syuhada, Fahmi Syuhada; Ahmad Fatoni Dwi Putra
SainsTech Innovation Journal Vol. 7 No. 1 (2024): SIJ VOLUME 7 NOMOR 1 TAHUN 2024
Publisher : LPPM Universitas Qamarul Huda Badaruddin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37824/sij.v7i1.2024.750

Abstract

penelitian ini bertujuan untuk mengembangkan media pembelajaran berbasis game untuk menyusun puzzle, dengan tema pahlawan Indonesia, menggunakan bahasa pemrograman JavaScript. Media pembelajaran ini dirancang untuk meningkatkan pemahaman dan minat siswa terhadap sejarah pahlawan Indonesia dengan cara yang interaktif dan menyenangkan. Pengembangan game ini melibatkan tahapan perancangan, pengkodean, dan pengujian untuk memastikan fungsionalitas yang optimal. Game ini memungkinkan siswa untuk menyusun puzzle gambar pahlawan Indonesia, yang diharapkan dapat meningkatkan keterampilan kognitif serta mengenalkan siswa pada nilai-nilai perjuangan pahlawan Indonesia. Hasil penelitian menunjukkan bahwa penggunaan game ini dapat meningkatkan motivasi belajar siswa dan memberikan pengalaman edukasi yang menarik serta efektif dalam memperkenalkan sejarah Indonesia. Penelitian ini juga membahas keunggulan penggunaan JavaScript dalam pengembangan game berbasis web yang dapat diakses dengan mudah oleh siswa.
Internet of Things-Based Automatic Trash Can Prototype Using Arduino Mega 2560 Sumarno Wijaya; Ahmad Fatoni Dwi Putra; Yuan Sa'adati; Hadi San, Ahmad Syahrul; Yunus, Muhajir; Talirongan, Florence Jean B.; G. Tangaro, Diana May Glaiza; Grancho, Bernadine
Indonesian Journal of Modern Science and Technology Vol. 1 No. 2 (2025): May
Publisher : CV. Abhinaya Indo Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64021/ijmst.1.2.43-49.2025

Abstract

The development of Internet of Things (IoT) technology encourages the creation of various smart device innovations that can be applied in everyday life, one of which is an automatic waste management system. This research aims to design and implement an IoT-based automatic trash can prototype using an Arduino Mega 2560 microcontroller that is able to detect the presence of people who will throw away garbage, open and close the lid of the tub automatically, and provide notification if the trash can is full. This research uses an experimental method by combining ultrasonic sensors, servo motors, and LED indicators as the main components. The test results show that the device works well and in accordance with the researcher's expectations. Ultrasonic sensor 1 can detect the presence of objects in front of the trash can and trigger the servo motor to open and close the lid automatically. Ultrasonic sensors 2 and 3 are also able to detect the height of the garbage and activate the servo motor while the indicator LEDs also function as designed: LED 1 blinks when someone approaches to take out the trash, while LED 2 and LED 3 light up when the sensors detect that the trash has reached a certain height limit. In addition, the system is energy efficient as it only activates when an object is detected, making it suitable for households and educational institutions.
Information Technology Governance Audit at the Communication and Information Office of Central Lombok Regency Using the COBIT 2019 Framework Sumiati; Joni Saputra; Ahmad Fatoni Dwi Putra
Indonesian Journal of Modern Science and Technology Vol. 1 No. 2 (2025): May
Publisher : CV. Abhinaya Indo Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64021/ijmst.1.2.34-42.2025

Abstract

Information technology is widely used to enhance the ability to deliver, manage, and distribute information. The objective of this study is to assess the capability and maturity levels as well as to provide recommendations and suggestions. This research was conducted using the COBIT 2019 framework standard. Among the process objectives, three relevant objectives were selected: APO07, APO08, and APO12. The results of the study indicate that APO07 is at capability level 1 with a maturity score of 79.62%. APO08 has a maturity score of 80%, while APO12 has a maturity score of 74.99%. All three objectives fall within the evidence work of product category “Largely Achieved” (50–84%).
Identifikasi Status Stunting menggunakan Metode Klasifikasi Pemrosesan Citra: Systematic Literature Review Putri, Mindi Richia; Putra, Ahmad Fatoni Dwi; Asmaul Husna; Arsan Kumala Jaya; Muhammad Ari Rifqi
Journal of Computer and Information System ( J-CIS ) Vol 8 No 1 (2025): J-CIS Vol. 8 No. 1 Tahun 2025
Publisher : Universitas Sulawesi Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31605/jcis.v8i1.5061

Abstract

Stunting adalah masalah kesehatan yang signifikan di Indonesia yang memengaruhi pertumbuhan fisik, perkembangan kognitif, dan kualitas sumber daya manusia di masa depan. Laporan dari Organisasi Kesehatan Dunia (WHO) menyatakan bahwa prevalensi stunting di Indonesia mencapai 21,6% pada tahun 2022. Untuk mengklasifikasikan stunting, metode konvensional seperti pengukuran antropometri manusal masih digunakan, tetapi memiliki keterbatasan seperti bergantung pada tenaga medis, memiliki kemungkinan kesalahan, dan sulit diakses di daerah terpencil. Tujuan dari penelitian ini adalah untuk mengevaluasi teknologi dan pemrosesan citra sebagai alternatif untuk metode deteksi stunting yang lebih akurat dan efektif. Hasil penelitian menunjukkan bahwa teknologi dan algoritma seperti MediaPipe Pose memiliki akurasi 98,48%, Deep Neural Nets (DNN) 93,83%, dan Support Vector Machine (SVM) 91,1%. Algortima CNN lebih efektif dalam menganalisis gambar secara otomatis terutama untuk dataset besa dan algortima SVM efektif untuk dataset kecil-menengah dengan dukungan ekstraksi fitur. Peneliti merekomendasikan untuk menggabungkan kedua metode ini untuk membuat sistem deteksi stunting yang lebih cepat, akurat, dan efisien. Temuan ini diharapkan dapat berfungsi sebagai titik acuan penting dalam proses pengembangan inovasi di bidang kesehatan anak di Indonesia.
PENGEMBANGAN MEDIA PEMBELAJARAN GAME MENYUSUN PUZZLE PAHLAWAN INDONESIA BERBASIS JAVASCRIPT Satrio Budi Santoso; Syuhada, Fahmi Syuhada; Ahmad Fatoni Dwi Putra
SainsTech Innovation Journal Vol. 7 No. 1 (2024): SIJ VOLUME 7 NOMOR 1 TAHUN 2024
Publisher : LPPM Universitas Qamarul Huda Badaruddin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37824/sij.v7i1.2024.750

Abstract

penelitian ini bertujuan untuk mengembangkan media pembelajaran berbasis game untuk menyusun puzzle, dengan tema pahlawan Indonesia, menggunakan bahasa pemrograman JavaScript. Media pembelajaran ini dirancang untuk meningkatkan pemahaman dan minat siswa terhadap sejarah pahlawan Indonesia dengan cara yang interaktif dan menyenangkan. Pengembangan game ini melibatkan tahapan perancangan, pengkodean, dan pengujian untuk memastikan fungsionalitas yang optimal. Game ini memungkinkan siswa untuk menyusun puzzle gambar pahlawan Indonesia, yang diharapkan dapat meningkatkan keterampilan kognitif serta mengenalkan siswa pada nilai-nilai perjuangan pahlawan Indonesia. Hasil penelitian menunjukkan bahwa penggunaan game ini dapat meningkatkan motivasi belajar siswa dan memberikan pengalaman edukasi yang menarik serta efektif dalam memperkenalkan sejarah Indonesia. Penelitian ini juga membahas keunggulan penggunaan JavaScript dalam pengembangan game berbasis web yang dapat diakses dengan mudah oleh siswa.
Optimizing Rain Prediction Model Using Random Forest and Grid Search Cross-Validation for Agriculture Sector Putra, Ahmad Fatoni Dwi; Azmi, Muhamad Nizam; Wijayanto, Heri; Utama, Satria; Wedashwara Wirawan, I Gede Putu Wirarama
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 3 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i3.3891

Abstract

Agriculture, as a sector that is highly influenced by weather conditions, faces challenges due to increasingly unpredictable changes in weather patterns. The aim of this research is to create an optimal rainfall prediction model to help farmers create irrigation schedules, use fertilizer, and planting schedules, and protect plants from extreme weather events. The method used in this research to obtain the best rain prediction model is to use the random forest algorithm and the grid search cross-validation algorithm. Random Forest, known for its robustness and accuracy, emerged as a suitable algorithm for predicting rain. utilizing a substantial dataset from the West Nusa Tenggara Meteorology, Climatology, and Geophysics Agency covering the period 2000 to 2023. The data is then processed first to ensure its readiness for use. This process involves removing outlier data points, empty data entries, and unused features. After the preprocessing stage, the data underwent training using the Random Forest algorithm, resulting in an R-squared value of 0.1334. To obtain the optimal model, Grid Search Cross Validation is used. The results of this research obtained the best rain prediction model with an R-squared value of 0.0268. This model will be used to predict rain in the agricultural sector. This research concludes that we can get the best rain prediction model by combining Random Forest and Gird Search Cross-Validation. For further research, we can compare other rain prediction methods, add features, and combine datasets from a wider area.
Text Mining untuk Analisis Kasus Stunting di Nusa Tenggara Barat Syuhada, Fahmi; Sa'adati, Yuan; Apriani, Lia Arian; Lonang, Syahrani; Putra, Ahmad Fatoni Dwi
EDUTIC Vol 12, No 1: 2025
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/edutic.v12i1.29522

Abstract

Pemberitaan stunting yang menjadi permasalahan nasional khususnya di provinsi Nusa Tenggara Barat (NTB) sudah masif tersedia pada dunia maya beberapa tahun terakhir. Oleh sebab itu analisis terhadap trend pemberitaan kasus ini sangat menarik dilakukan. Tujuannya yaitu melihat istilah-istilah kata yang berhubungan dengan kata pada pemberitaan stunting. Dengan ini akan diketahui istilah-istilah atau kata-kata dan pola komunikasi publik terkait isu stunting di NTB. Penelitian ini  mengusulkan penerapan teknik Text Mining dalam menganalisis trend stunting NTB pada pemberitaan dunia maya. Data dikoleksi dari portal berita dengna query yang berkaitan dengan stunting NTB dari tahun 2018 hingga 2024. Penerapan metode text mining seperti preprocessing, ekslorasi data (EDA) dan Latent Dirichlet Allocation (LDA) hingga visualisasi hasil digunakan untuk reprensi trend tersebut. Kontribusi penelitian difokuskan pada bagaimana analisis trend berdasarkan tiga kumpulan corpus yaitu trend Utama Stunting, Sebab, dan Dampak. Analisis tren NTB menunjukkan bahwa istilah stunting, gizi, dan air bersih mendominasi pemberitaan, mencerminkan fokus pada faktor kesehatan utama dalam pencegahan stunting. Faktor sosial seperti nikah muda dan pendidikan juga memiliki hubungan signifikan, menunjukkan perlunya pendekatan yang mencakup dimensi sosial dan budaya. Sementara itu, intervensi berbasis komunitas, seperti posyandu, berperan penting dalam mendukung edukasi dan pemantauan gizi anak. Namun, istilah geografis seperti Bima, meskipun sering muncul, tidak memiliki hubungan langsung dengan stunting, melainkan lebih terkait dengan konteks administratif. Keseluruhan analisis menegaskan perlunya pendekatan terintegrasi yang melibatkan faktor kesehatan, sosial, dan komunikasi publik yang efektif untuk menekan prevalensi stunting di NTB.
Co-Authors Adipta, Marazaenal Afriansyah, M. Anggarista, Randa Arsan Kumala Jaya Asmaul Husna Asno Azzawagama Firdaus Azmi, Muhamad Nizam G. Tangaro, Diana May Glaiza Grancho, Bernadine Hadi San, Ahmad Syahrul Heri Wijayanto Heri Wijayanto I Gede Putu Wirarama Wedashwara Wirawan Joni Saputra Joni Saputra Kumoro, Danang Tejo Mindi Richia Putri Muhajir Yunus Muhamad Nizam Azmi Muhamad Yamin Muhammad Ari Rifqi Pratama, Ramadhana Agung Sa'adati, Yuan Satria Utama Satria Utama, Satria Satrio Budi Santoso Sumarno Wijaya Sumiati Syahrani Lonang Syuhada, Fahmi Syuhada, Fahmi Syuhada Talirongan, Florence Jean B. Wedashwara Wirawan, I Gede Putu Wirarama Yuan Sa'adati