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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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Journal : Progresif: Jurnal Ilmiah Komputer

 1 
Penerapan Jaringan Saraf Tiruan Backpropagation Dalam Memprediksi Nilai Tukar Petani Wahyudi Ariannor; Muhammad Fajar Razatillah
Progresif: Jurnal Ilmiah Komputer Vol 18, No 1: Februari 2022
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (559.647 KB) | DOI: 10.35889/progresif.v18i1.798

Abstract

Abstrak. Nilai Tukar Petani (NTP) merupakan alat untuk mengukur kemampuan tukar produk yang dijual petani dengan produk yang dibutuhkan petani dalam produksi dan konsumsi rumah tangga. NTP khususnya pada subsektor Tanaman Pangan, seringkali berfluktuasi setiap bulannya, sehingga dipandang perlu untuk diprediksi dengan tepat, agar dapat membantu pemerintah dan pihak terkait mempersiapkan tindakan-tindakan pencegahan seperti menjaga kestabilan harga produksi pertanian dan mengendalikan harga-harga biaya usaha pertanian. Paper ini menguji penerapan Jaringan Syaraf Tiruan (JST) Backpropagation untuk memprediksi NPT pada sub sektor Tanaman Pangan di Provinsi Kalimantan Selatan. Pengujian dilakukan menggunakan 84 data latih dan 36 data uji. Data masukan berupa data deret waktu NPT subsektor Pangan Provinsi Kalimantan Selatan selama 12 bulan sebelumnya untuk memprediksi NPT setiap bulannya selama periode 12 bulan mendatang. Hasil uji menunjukkan nilai presentase error (MAPE) 0,97, atau diperoleh persentase akurasi prediksi sebesar 99,03 %.Kata Kunci: Prediksi; Nilai Tukar Petani; Subsektor Tanaman Pangan; Jaringan Syaraf tiruan; Backpropagation Abstract. Farmer's Exchange Rate  is a tool to measure the ability to exchange products sold by farmers with products needed by farmers in household production and consumption. Farmer's Exchange Rate, especially in the Food Crops sub-sector, often fluctuates every month, so it is deemed necessary to predict accurately, in order to assist the government and related parties in preparing preventive measures such as maintaining stability in agricultural production prices and controlling agricultural costs. This paper examines the application of Backpropagation Artificial Neural Networks to predict Farmer's Exchange Rate in the Food Crops sub-sector in South Kalimantan Province. The test was carried out using 84 training data and 36 test data. The input data is in the form of time series data for the Farmer's Exchange Rate of the Food sub-sector of South Kalimantan Province for the previous 12 months to predict the Farmer's Exchange Rate every month for the next 12 month period. The test results show the percentage error value (MAPE) is 0.97, or the percentage of prediction accuracy is 99.03%.Keywords: Prediction; Farmer's Exchange Rate; Food Crops Subsector; Artificial Neural Networks; Backpropagation
Analyzing User Sentiments in Motor Vehicle Tax Applications Using the Naïve Bayes Algorithm Wahyudi Ariannor; Erwin Arry Kusuma; Fadilah Fadilah; Muhammad Arsyad
Progresif: Jurnal Ilmiah Komputer Vol 20, No 1: Februari 2024
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v20i1.1694

Abstract

The South Kalimantan Tax Info application service can provide information about the amount of tax, when you have to pay tax, the due date, and so on. However, the South Kalimantan Tax Info application received many negative reviews from users. So it is necessary to analyze user sentiment using computational techniques. In this context, sentiment analysis is applied using the Naïve Bayes method to user reviews, assisting the Regional Revenue Agency in understanding perceptions and enhancing service quality. The literature review encompasses similar studies that employ the Naïve Bayes algorithm for sentiment analysis in e-government applications. The research methodology involves collecting review data from the Google Play Store through web scraping, labeling based on ratings, and pre-processing. The results of sentiment analysis, utilizing the confusion matrix, demonstrate the highest accuracy of 92% with a 10:90 data split. This study contributes to users' comprehension of public service applications, facilitating continuous improvement.Keywords: Sentiment analysis; Confusion matrix; Text mining; Emoticon AbstrakLayanan aplikasi Info Pajak Kalsel dapat memberikan informasi tentang besaran pajak, waktu harus bayar pajak, jatuh tempo dan lain-lain.  Namun, aplikasi Info Pajak Kalsel menerima banyak ulasan negatif dari pengguna. Maka diperlukan analisis sentimen pengguna menggunakan teknik komputasi. Dalam konteks ini, analisis sentimen diterapkan menggunakan metode Naïve Bayes pada ulasan pengguna, membantu Badan Pendapatan Daerah memahami persepsi dan meningkatkan kualitas layanan. Studi literatur mencakup penelitian sejenis yang menggunakan algoritma Naïve Bayes untuk analisis sentimen pada aplikasi e-Government. Metodologi penelitian melibatkan pengumpulan data ulasan dari Google Playstore melalui web scraping, pemberian label berdasarkan rating, dan proses pre-processing. Hasil analisis sentimen menggunakan confusion matrix menunjukkan akurasi tertinggi sebesar 92% pada pembagian data 10:90. Studi ini memberikan kontribusi pada pemahaman pengguna terhadap aplikasi pelayanan publik, memungkinkan perbaikan berkelanjutan.Kata kunci: Analisis sentimen; Matriks konfusi; Text mining; Emotikon
Analisis Kinerja Model Machine learning dalam Prediksi Gagal Panen Gabah Nizami, Taufik; Mustaqiim, Muhammad Atillah; Ariannor, Wahyudi
Progresif: Jurnal Ilmiah Komputer Vol 21, No 1: Februari 2025
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
Web-Based Geographic Information System Model for Construction Business Surveys Fathimah, Siti; Abdi, Syarifullah; Ariannor, Wahyudi
Progresif: Jurnal Ilmiah Komputer Vol 21, No 1: Februari 2025
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
Co-Authors Abdi, Syarifullah Ahriza, Muhammad Akbar, Rizky Fadila Muhammad Akhmad Zakirin 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 Fajar Razatillah Muhammad Imam Mulyana Muhrian Noor Mustaqiim, Muhammad Atillah Musthofa, Ahmadal Noor, Muhrian Novaliah Novaliah Pahdi, Ahmad Pratiwi, Andita Suci Ratna Fitriani Rizki, Ahmad Romiyadi, Rizar Rustati Rahmi Sami M A B Alshalwi Setyo Wahyu Saputro Siti Abidah Siti Fathimah Taufik Nizami Yudihartanti, Yulia