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All Journal Jurnal Transformatika JIEET (Journal of Information Engineering and Educational Technology) Jurnal Pengabdian Masyarakat Julia Jurnal
Andri Triyono
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Humanities Computer Science & IT Education Engineering Languange, Linguistic, Communication & Media Public Health Other

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IMPLEMENTASI ALGORITMA APRIORI UNTUK MENCARI POLA TRANSAKSI PENJUALAN PADA TOKO PERTANIAN TOKO BIDSALTANI Muhamad Nuryahya; Andri Triyono; Agus Susilo Nugroho
Julia: Jurnal Ilmu Komputer An Nuur Vol 4 No 1 (2024): Julia Jurnal
Publisher : LPPM Universitas An Nuur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35720/julia.v4i1.20

Abstract

Progress in the industrial sector is currently growing rapidly, especially in medium and upper-class businesses, especially in agricultural shop businesses. Agricultural shops are one of the medium-sized businesses where competition is quite tight, this can be seen from the high consumer demand for fertilizer and agricultural equipment.With the high demand of consumers for agricultural needs as well as intense competition, agricultural shop companies must further improve their business performance in order to be able to face the problems that occur.Bidsal Tani is one of the many agricultural shops in Purwodadi District that sells agricultural necessities, such as chemical fertilizer, compost, plant seeds and all other agricultural necessities, it can be seen that to make a profit as expected.The a priori algorithm is a market basket analysis algorithm used to produce association rules. Association rules can be used to find relationships or cause and effect. The results of the research are that the products frequently purchased by consumers are PHONSKA, NPA, ZA, FASTAC, KOGE, UREA, GANDASIL, FLORAN, SP36, TSP, WUXAL, BAYFOLAN, BLOPATEK, KCL, HYDRASIL AND DECIS products.
PENERAPAN SISTEM INFORMASI STOK BARANG BERBASIS APLIKASI UNTUK MENINGKATKAN EFISIENSI PENGELOLAAN INVENTARIS PADA TOKO SEMBAKO Agus Condro Wibowo; Dwi Kurniawan Aprilianto; Ahmad Yususf Mufarihin; Andri Triyono; Dhika Malita Puspita Arum
Julia: Jurnal Ilmu Komputer An Nuur Vol 5 No 1 (2025): Julia Jurnal
Publisher : LPPM Universitas An Nuur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35720/julia.v5i1.22

Abstract

Perkembangan teknologi informasi yang berkembang pesat memberikan dampak yang signifikan ke berbagai sektor, seperti pengelolaan inventaris di sektor ritel. penelitian ini bertujuan untuk menerapkan sistem informasi yang berbasis aplikasi untuk stock barang agar meningkatkan efisiensi pengelolaan inventaris pada toko. aplikasi yang di bangun di harapkan dapat mempermudah pemantauan stok barang, mempercepat proses pencatatan transaksi, serta meminimalisir kesalahan dalam mengelola data stock. metode yyang di gunakan dalam penelitian ini adalah pengembangan aplikasi berbasis perangkat lunak yang dapat memberikan hasil laporan inventaris secara akurat. hasil yang di harapkan dari penerapan sistem informasi ini adalah pengurangan tingkat kesalahan, serta pengelolaan stok yang lebih mudah dan cepat. Penelitian ini memiliki kontribusi dalam memberikan solusi bagi toko-toko untuk menghadapi tantangan pengelolaan inventaris yang lebih kompleks di era digital sekarang. 
PREDIKSI LUAS PANEN DI KECAMATAN PURWOADADI MENGGUNAKAN ALGORITMA REGRESI LINEAR BERGANDA Muhammad Akbar Mustofa; Andri Triyono; Agus Susilo Nugroho
Julia: Jurnal Ilmu Komputer An Nuur Vol 5 No 1 (2025): Julia Jurnal
Publisher : LPPM Universitas An Nuur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35720/julia.v5i1.23

Abstract

Agriculture, particularly rice cultivation, is highly vulnerable to climate change because it depends on water cycles and weather conditions to maintain productivity. Climate change affects crop growth, development, and yields, as agricultural activities are heavily dependent on weather and climate. This study utilizes data mining to introduce a new breakthrough in addressing rice farming issues in Grobogan Regency, Purwodadi District. The method used is multiple linear regression, with the dependent variable being harvested area and the independent variables including plxanted area and rainfall. The objective of this research is to test and develop data mining methods to predict yield levels, thereby assisting local governments in decision-making during crop failures, based on agricultural data from 20192023. The research process involves data collection, preprocessing, algorithm implementation, and result evaluation. The analysis shows that the multiple linear regression model provides reasonably accurate predictions, with a Root Mean Square Error (RMSE) value of 209.042 and a Relative Root Squared Error (RRSE) of 0.111. Furthermore, the analysis reveals that planted area significantly influence the harvested area. These findings offer insights for local governments as policymakers in providing aid during crop failures. 
Expert System For Corn Plant Disease Diagnosis Using Hybrid Fuzzy Tsukamoto And Naive Bayes Method Kartika Imam Santoso; Eko Supriyadi; Andri Triyono; Dhika Malita Puspita
JIEET (Journal of Information Engineering and Educational Technology) Vol. 9 No. 2 (2025)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jieet.v9n2.p141-155

Abstract

Corn is a strategic food commodity in Indonesia, with production of 22.44 million tons in 2023. However, disease attacks can cause productivity declines of up to 30-80%, mainly from downy mildew, leaf rust, and leaf spot. The limited number of pathology experts in the field leads to delayed diagnosis, resulting in significant economic losses for farmers. This research aims to develop an expert system for diagnosing corn plant diseases using a hybrid Fuzzy Tsukamoto and Naive Bayes method to enhance diagnosis accuracy, taking into account uncertainty in symptom severity levels. The system was developed using Durkin's Expert System Development Life Cycle (ESDLC), which consists of six phases. A knowledge base was built from SINTA and Scopus-indexed literature, identifying five diseases and 17 symptoms. The fuzzy Tsukamoto method was employed for the fuzzification of symptom severity, utilizing three membership functions (intensity, coverage, and severity), after which Naive Bayes calculated the posterior probability. The hybrid score was calculated with 40% Fuzzy and 60% Bayes weights. The system was successfully developed with an interactive web interface. Accuracy testing using 30 validation cases yielded an accuracy of 86.67%, with 85% sensitivity and 88% specificity. Expert testing by three plant pathology experts gave excellent ratings (average 4.6/5.0) for diagnosis accuracy, knowledge base completeness, and usability aspects. The hybrid Fuzzy Tsukamoto and Naive Bayes method is effective for diagnosing corn plant diseases, achieving 86.67% accuracy, which is 6.67% higher than the Certainty Factor method and 11.67% higher than the single Naive Bayes method. This system can help farmers perform early diagnosis and reduce dependence on experts.
Co-Authors Agus Condro Wibowo Agus Susilo Nugroho Ahmad Yususf Mufarihin Alfianaa khanifiyah Arum, Dhika Malita Puspita Dhika Malita Dhika Malita Dhika Malita Puspita Dwi Kurniawan Aprilianto Edi Widodo, Edi Eko Supriyadi Happy Dewi Ariyantini Kartika Imam Santoso Mika Agustiana Muhamad nuryahya Muhammad Akbar Mustofa Rahmawan Bagus Trianto Retika nur fadila susilo nugroho, agus WAHYUDI