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All Journal Jurnal TIPS : Jurnal Teknologi Informasi dan Komputer Politeknik Sekayu RESOLUSI : REKAYASA TEKNIK INFORMATIKA DAN INFORMASI Journal of Informatics Management and Information Technology
Cindi Wulandari
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Computer Science & IT

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PERENCANAAN STRATEGIS SISTEM INFORMASI DAN TEKNOLOGI INFORMASI PADA STIE MURA LUBUKLINGGAU MENGGUNAKAN METODE WARD AND PEPPARD Cindi Wulandari
Jurnal TIPS : Jurnal Teknologi Informasi dan Komputer Politeknik Sekayu Vol 6 No 1 (2017): Jurnal TIPS
Publisher : Politeknik Sekayu

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1385.121 KB)

Abstract

Perencanaan Strategis Sistem Informasi dan Teknologi Informasi merupakan turunan dari RencanaStrategis (Renstra) sebuah institusi. Tujuan dari riset ini adalah menemukan kebutuhan SistemInformasi dan Teknologi Informasi (SI/TI) untuk Sekolah Tinggi studi kasus terhadap Renstra STIEMURA Lubuklinggau. Riset diawali dengan mengevaluasi renstra organisasi dari STIE MURALubuklinggau, analisis lingkungan bisnis eksternal dan internal STIE MURA Lubuklinggau gunamenentukan PEST model dan critical success factors Sekolah Tinggi. Selanjutnya adalah analisislingkungan eksternal dan internal SI/TI guna mengetahui trend teknologi dunia dan currentapplication porfolio institusi. Tahap berikutnya adalah proses strategi (SI, TI, dan manajemen SI/TI).Tahap akhir adalah menentukan aplikasi-aplikasi SI/TI masa depan. Hasil yang dicapai adalahrekomendasi portofolio aplikasi SI/TI yang seharusnya dimiliki STIE MURA Lubuklinggau.
Klasifikasi Data Mining Pada Bibit Pertanian Dengan Menggunakan Algoritma Naïve Bayes Pandu Rahmat Aprianto; Novi Lestari; Cindi Wulandari
Resolusi : Rekayasa Teknik Informatika dan Informasi Vol. 5 No. 1 (2024): RESOLUSI September 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/resolusi.v5i1.2172

Abstract

Choosing the right oil palm seedlings is one of the efforts to increase the productivity of oil palm plants. At the time of seedling selection, the problem that is often faced is that not all types of seeds can be as desired by farmers against the conditions of different types of oil palm fruit seeds. Therefore, a system is needed that helps in determining the type of superior seedlings according to the needs of farmers. In this research the author applies a machine learning prediction model in determining oil palm seedlings based on leaf type, stem type, seed origin and stem candidates. The purpose of this research is to produce a classification model for determining oil palm seedlings and can provide accurate training for farmers in choosing superior seeds and also can farmers know the characteristics of superior seeds. The method used in this research is naïve bayes. In the first test, both models managed to achieve an accuracy of 84% and an F1-score value of 66%. However, the best performing Naïve Bayes model is the one used in the second test scenario, which is applied as a prediction model in determining oil palm seedlings through the website developed in this study, in the form of a Data Mining Classification System on Agricultural Seedlings at the Agricultural Extension Center of Tuah Negeri District, Musi Rawas Regency Based on Website which can assist in the selection process of oil palm seedlings that will be planted and get superior seedling results.
Prediksi Padi Menggunakan Algoritma Long Short Term Memory Adhany, Putri Cheria; Cindi Wulandari; Bunga Intan; Budi Santoso
Journal of Informatics Management and Information Technology Vol. 5 No. 2 (2025): April 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jimat.v5i2.496

Abstract

Rice is one of the main agricultural commodities in Indonesia, including in Lubuklinggau City, which is a rice-producing area in South Sumatra Province. However, rice production fluctuates every month due to various factors such as planting seasons, land conversion, weather, and pest attacks. This instability can affect food availability and farmer welfare. Therefore, rice production forecasting is important in supporting better decision-making in the agricultural sector. This study uses monthly rice production data from January 2019 to November 2024 obtained from the Lubuklinggau City Agriculture Service. The method used is Long Short-Term Memory (LSTM), which is one of the artificial neural network techniques based on time series data. The optimal parameters used in the model are the number of neurons in the hidden layer of 35, a batch size of 12, and a maximum of 50 epochs. The results showed that the model with optimal parameters produced a Mean Absolute Percentage Error (MAPE) value of 4.44%, which is included in the very good category. These results indicate that the LSTM method can be used effectively to predict rice production in Lubuklinggau City with a high level of accuracy.
Co-Authors Adhany, Putri Cheria Budi Santoso Bunga Intan Novi Lestari Pandu Rahmat Aprianto