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All Journal IJIE (Indonesian Journal of Informatics Education) Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Progresif: Jurnal Ilmiah Komputer Jurnal Pengabdian Masyarakat - Teknologi Digital Indonesia Jurnal Pendidikan dan Pengajaran
Wahyudi Ariannor
STMIK BANJARBARU
Religion Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Environmental Science Industrial & Manufacturing Engineering Social Sciences Other

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Analisis Kinerja Model Machine learning dalam Prediksi Gagal Panen Gabah Taufik Nizami; Muhammad Atillah Mustaqiim; Wahyudi Ariannor
Progresif: Jurnal Ilmiah Komputer Vol 21, No 1 (2025): Februari
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v21i1.2501

Abstract

In Banjar Regency, rice production faces significant challenges, including high crop failure rates and production variability across regions, which impact equitable food availability. This study aims to analyze the performance of various machine learning algorithms in predicting rice crop failures, a critical issue in food security. The research variables include factors such as weather, air humidity, soil conditions, agricultural variables, and tungro disease infestations. Several algorithms were tested, including Naive Bayes, Logistic Regression, Decision Tree, Random Forest, XGBoost, and others. Evaluation was conducted using cross-validation techniques with metrics such as accuracy, precision, recall, F1-Score, and ROC AUC. The results indicate that the Random Forest and XGBoost algorithms achieved the best performance, with accuracies of 77% and 70%, respectively. The study concludes that machine learning-based models can support better decision-making to mitigate crop failure risks. Furthermore, this research provides a foundation for the development of predictive models in the agricultural sector.Keywords: Harvest failure; Rice; Machine learning; Prediction; Food security AbstrakDi Kabupaten Banjar, produksi gabah menghadapi kendala signifikan, termasuk gagal panen yang tinggi dan variasi produksi antar wilayah, yang memengaruhi ketersediaan pangan merata. Penelitian ini bertujuan untuk menganalisis kinerja berbagai algoritma machine learning dalam memprediksi gagal panen gabah, yang merupakan permasalahan penting dalam ketahanan pangan. Variabel penelitian mencakup faktor-faktor seperti cuaca, kelembapan udara, kondisi tanah, variabel pertanian, dan serangan tungro. Beberapa algoritma yang diuji meliputi Naive Bayes, Logistic Regression, Decision Tree, Random Forest, XGBoost, dan lainnya. Evaluasi dilakukan menggunakan teknik cross-validation dengan metrik akurasi, precision, recall, F1-Score, dan ROC AUC. Hasil menunjukkan bahwa algoritma Random Forest dan XGBoost memberikan performa terbaik, dengan akurasi masing-masing sebesar 77% dan 70%. Kesimpulan penelitian ini menunjukkan bahwa model berbasis machine learning dapat digunakan untuk mendukung pengambilan keputusan yang lebih baik dalam mengurangi risiko gagal panen. Penelitian ini juga memberikan dasar untuk pengembangan model prediksi di sektor agrikultur.Kata kunci: Gagal panen; Gabah; Machine learning; Prediksi; Ketahanan pangan
Sentiment Analysis of Netizens on Constitutional Court Rulings in the 2024 Presidential Election Wahyudi Ariannor; Sami M A B Alshalwi; Budi Susarianto
IJIE (Indonesian Journal of Informatics Education) Vol 8, No 2 (2024): (IJIE) Indonesian Journal of Informatics Education - December
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijie.v8i2.94614

Abstract

AbstractOnline conversations among netizens play an important role in forming collective opinions and views about important events, including judicial decisions such as those taken by the Constitutional Court (MK). This research explores sentiment analysis of the Constitutional Court’s decisions, especially in the context of the presidential election, using the Support Vector Machine (SVM), Logistic Regression, and Naive Bayes algorithms. Previous studies on public sentiment toward the Constitutional Court’s decision provide a basis. Still, this research focuses on a different context, analysing sentiment toward the Constitutional Court’s decision in the 2024 presidential election dispute. This study adopts an experimental methodology, involving several key stages such as data collection through Twitter web scraping, labelling, pre-processing, TF-IDF weighting, and algorithm testing. Evaluation using a confusion matrix shows comparable accuracy among SVM, Logistic Regression, and Naive Bayes, with SVM and Logistic Regression demonstrating superior precision and F1 scores. Negative sentiment carries greater weight than neutral and positive sentiment, highlighting potential social tensions and the need for effective communication and deeper analysis to understand the root causes of negativity. The SVM and logistic regression algorithms have proven effective in understanding public sentiment towards the Constitutional Court’s decisions in a political context, providing valuable insights for understanding the dynamics of public opinion.
MODEL APLIKASI REKOMENDASI CALON PENERIMA BANTUAN WARGA DISABILITAS DENGAN METODE ARAS Maulana, Arpan; Abidah, Siti; Ariannor, Wahyudi; Mulyani, Dwi
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol. 15 No. 3 (2026): Juni 2026
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v15i3.3880

Abstract

The process of data collection and aid distribution for persons with disabilities in Loktabat Selatan Sub-district is currently still conducted manually. This leads to various obstacles, such as vulnerability to data duplication, recording errors, taking considerable time during the verification process, and recommendations for aid recipients that are not yet fully objective. Therefore, this research aims to design and build a web-based Recommendation Application for Prospective Disability Aid Recipients to facilitate and improve the accuracy of the selection process. This study uses the Additive Ratio Assessment (ARAS) method as a decision support system to rank prospective recipients of aid, which may include cash assistance, assistive devices, and other forms of support. The assessment is based on eight criteria: type of disability, age, occupation, marital status, housing status, number of dependents, total monthly income, and history of receiving assistance. System testing was conducted using Blackbox Testing and User Acceptance Testing (UAT). The result of this research is a web-based application capable of facilitating the data collection, verification, and recommendation of prospective aid recipients in a centralized, systematic, and objective manner. Based on the UAT, the application received a highly positive response as it proved to simplify data collection, accelerate the selection process, and facilitate report generation. Keywords: Disability; Social Aid; Decision Support System; ARAS; Web.   Abstrak Proses pendataan dan penyaluran bantuan bagi warga penyandang disabilitas di Kelurahan Loktabat Selatan saat ini masih dilakukan secara manual. Hal ini menimbulkan berbagai kendala, seperti rentannya duplikasi data, kesalahan pencatatan, cukup memakan waktu ketika proses verifikasi, serta rekomendasi penerima bantuan yang belum sepenuhnya objektif. Oleh karena itu, penelitian ini bertujuan untuk merancang dan membangun Aplikasi Rekomendasi Calon Penerima Bantuan Warga Disabilitas berbasis web guna mempermudah dan meningkatkan akurasi proses seleksi tersebut. Penelitian ini menggunakan metode Additive Ratio Assessment (ARAS) sebagai sistem pendukung keputusan untuk merangking calon penerima bantuan yang dapat berupa dana tunai, alat bantu, dan sebagainya. Penilaian didasarkan pada delapan kriteria, yaitu jenis disabilitas, umur, pekerjaan, status perkawinan, status rumah, jumlah tanggungan, jumlah penghasilan bulanan, dan riwayat penerimaan bantuan. Pengujian sistem dilakukan melalui Blackbox Testing dan User Acceptance Testing (UAT). Hasil penelitian ini adalah sebuah aplikasi berbasis web yang mampu memfasilitasi pendataan, verifikasi, dan pemberian rekomendasi calon penerima bantuan secara terpusat, sistematis, dan objektif. Berdasarkan pengujian UAT, aplikasi ini mendapat respons yang sangat positif karena terbukti mempermudah pendataan, mempercepat seleksi, serta memudahkan pembuatan laporan. Kata kunci: Disabilitas; Bantuan Sosial; Sistem Pendukung Keputusan; ARAS; Web
MODEL APLIKASI PEMBELAJARAN HURUF HIJAIYAH DI TAMAN PENDIDIKAN AL-QUR'AN HALABY BANJARBARU Siti Dzakia Salsabila; Khairullah Khairullah; Wahyudi Ariannor; Muhammad Arsyad
Progresif: Jurnal Ilmiah Komputer Vol. 22 No. 3 (2026): Juli
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v22i3.3648

Abstract

Learning hijaiyah letters at Taman Pendidikan Al-Qur’an Halaby Banjarbaru previously faced challenges such as low student interest and an unconducive classroom environment due to conventional teaching methods. This study aimed to design and develop a web-based hijaiyah learning application model that enhances interactivity while supporting learning management. The method used was Research and Development with a Waterfall system development model consisting of requirements analysis, design, implementation, and testing stages. The developed application included learning materials, interactive quizzes, teacher and student data management, and learning outcome recording features. The testing results showed that all system functions operated properly, and the application improved learning outcomes for most students. The novelty of this study lies in the integration of interactive learning media and learning management systems within a single platform. Keywords: Hijaiyah Learning; Web Application; Learning Media; Quran Education   Abstrak Pembelajaran huruf hijaiyah di Taman Pendidikan Al-Qur’an Halaby Banjarbaru sebelumnya menghadapi kendala berupa rendahnya minat belajar dan kurang kondusifnya suasana kelas akibat metode pembelajaran yang masih konvensional. Penelitian ini bertujuan untuk merancang dan membangun model aplikasi pembelajaran huruf hijaiyah berbasis web yang mampu meningkatkan interaktivitas serta mendukung pengelolaan pembelajaran. Metode yang digunakan adalah Research and Development dengan model pengembangan sistem Waterfall yang meliputi tahapan analisis kebutuhan, perancangan, implementasi, dan pengujian. Aplikasi yang dikembangkan dilengkapi dengan fitur materi pembelajaran, kuis interaktif, pengelolaan data guru dan peserta didik, serta pencatatan hasil belajar. Hasil pengujian menunjukkan bahwa seluruh fitur sistem berfungsi dengan baik dan penerapan aplikasi memberikan peningkatan hasil belajar pada sebagian besar peserta didik. Kebaruan penelitian ini terletak pada integrasi antara media pembelajaran interaktif dan sistem pengelolaan pembelajaran dalam satu platform.
STRATEGI EFEKTIF PELATIHAN MICROSOFT OFFICE UNTUK MENINGKATKAN KOMPETENSI SANTRIWATI PONDOK PESANTREN Yulia Yudihartanti; Muhammad Arsyad; Erwin Arry Kusuma; Wahyudi Ariannor; Eka Chandra Kirana; Siti Abidah; Khairullah Khairullah; Budi Susarianto; Muhammad Baihaqi; Ahmadal Musthofa
Jurnal Pengabdian Masyarakat - Teknologi Digital Indonesia. Vol 4, No 1 (2025): Maret 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jpm.v4i1.1860

Abstract

RingkasanPondok Pesantren Salafiyah Darussalam Martapura menyediakan pendidikan non-formal bagi santriwati yang ingin memperoleh keterampilan tambahan untuk mendukung pendidikan dan pekerjaan di masa depan. Namun, keterbatasan pengetahuan santriwati dalam penggunaan computer-based office applications menjadi tantangan dalam kesiapan mereka menghadapi dunia kerja atau perguruan tinggi. Untuk mengatasi hal ini, dilakukan pelatihan Microsoft Office yang mencakup Microsoft Word, Excel, dan PowerPoint. Pelatihan ini terdiri dari tahap persiapan, penyusunan modul, praktik langsung, serta evaluasi guna mengukur peningkatan keterampilan peserta. Hasil evaluasi menunjukkan peningkatan signifikan dengan rata-rata peningkatan skor sebesar 69,6%, serta kepuasan peserta yang mencapai 94%. Meskipun menghadapi kendala seperti keterbatasan fasilitas komputer dan durasi pelatihan yang terbatas, kegiatan ini terbukti efektif dalam meningkatkan kompetensi santriwati. Sebagai tindak lanjut, disarankan adanya pelatihan lanjutan dan pengembangan keterampilan teknologi lainnya guna memperluas manfaat bagi santriwati. Dengan adanya program ini, diharapkan santriwati lebih siap menghadapi tantangan pendidikan dan dunia kerja yang semakin mengandalkan teknologi.
Co-Authors Abidah, Siti Ahmadal Musthofa Ahriza, Muhammad Akbar, Rizky Fadila Muhammad Akhmad Zakirin Azmi, Muhammad Budi Susarianto Devia Rahma Safitri Dwi Mulyani Fadilah, Fadilah Fitriyadi, Fitriyadi Handayani, Tia Selvina Khairullah Khairullah Khairullah, Khairullah Khairullah, Khairullah Kirana, Eka Chandra Kusuma, Erwin Arry Maulana, Arpan Maulana, Dicky Maulana, Muhammad Fajar Muhammad Arsyad Muhammad Atillah Mustaqiim Muhammad Baihaqi Muhammad Fajar Razatillah Muhammad Imam Mulyana Muharnadiah, Siti Aisyah Muhrian Noor Noor, Muhrian Novaliah Novaliah Pahdi, Ahmad Pratiwi, Andita Suci Rahmi, Rustati Ratna Fitriani Rizki, Ahmad Romiyadi, Rizar Rustati Rahmi Sami M A B Alshalwi Setyo Wahyu Saputro Siti Abidah Siti Dzakia Salsabila Siti Fathimah Syarifullah Abdi Taufik Nizami Yudihartanti, Yulia Yulia Yudihartanti