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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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Web-Based Geographic Information System Model for Construction Business Surveys Siti Fathimah; Syarifullah Abdi; 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.v21i1.2535

Abstract

The construction business survey conducted by BPS Banjarbaru City frequently encounters challenges such as unclear business addresses, including the absence of house numbers or RT/RW, and annual changes of survey personnel, resulting in inconsistencies in data collection. This study aims to develop a web-based Geographic Information System (GIS) to address these issues. The Rapid Application Development (RAD) method was employed to ensure the system meets user requirements. Functional testing results demonstrate that all features evaluated operate as intended, ensuring the system supports survey efficiency, addresses issues with unclear addresses, and maintains data continuity across survey periods. Thus, the system provides an effective solution to the challenges faced in construction business surveys conducted by BPS Banjarbaru City.Keywords: Geographic Information System; Business entity survey; Construction AbstrakSurvei badan usaha konstruksi yang dilakukan oleh BPS Kota Banjarbaru sering menghadapi kendala berupa alamat badan usaha yang kurang jelas, seperti tidak tercantumnya nomor rumah atau RT/RW, serta pergantian petugas survei setiap tahun yang menyebabkan ketidakkonsistenan dalam pendataan. Penelitian ini bertujuan untuk mengembangkan Sistem Informasi Geografis (SIG) berbasis web sebagai solusi untuk mengatasi permasalahan tersebut. Metode Rapid Application Development (RAD) diterapkan untuk memastikan sistem sesuai dengan kebutuhan pengguna. Hasil pengujian fungsionalitas sistem menunjukkan bahwa, seluruh fitur yang diuji berfungsi sesuai dengan kebutuhan pengguna, memastikan sistem dapat mendukung efisiensi survei, mengatasi kendala alamat tidak jelas dan menjaga kesinambungan data antarperiode survei. Dengan demikian, sistem ini memberikan solusi permasalahan dalam survei badan usaha konstruksi di BPS Kota Banjarbaru.Kata kunci: Sistem Informasi Geografis; Survei badan usaha; Konstruksi
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 Ariannor, Wahyudi; Alshalwi, Sami M A B; Susarianto, Budi
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.
Co-Authors Ahriza, Muhammad Akbar, Rizky Fadila Muhammad Akhmad Zakirin Alshalwi, Sami M A B Azmi, Muhammad Budi Susarianto Devia Rahma Safitri Fachry Abda El Rahman Fadilah, Fadilah Fitriyadi, Fitriyadi Handayani, Tia Selvina Khairullah, Khairullah Khairullah, Khairullah Kirana, Eka Chandra Kusuma, Erwin Arry Kusuma, Erwin Arry Maulana, Dicky Maulana, Muhammad Fajar Muhammad Arsyad Muhammad Atillah Mustaqiim Muhammad Fajar Razatillah Muhammad Imam Mulyana Muharnadiah, Siti Aisyah Muhrian Noor Musthofa, Ahmadal Noor, Muhrian Novaliah Novaliah Pahdi, Ahmad Pratiwi, Andita Suci Rahmi, Rustati Ratna Fitriani Rizki, Ahmad Romiyadi, Rizar Rustati Rahmi Setyo Wahyu Saputro Siti Abidah Siti Fathimah Susarianto, Budi Syarifullah Abdi Taufik Nizami Yudihartanti, Yulia