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All Journal Semantik Seminar Nasional Informatika (SEMNASIF) JOIN (Jurnal Online Informatika) JOIV : International Journal on Informatics Visualization Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Komputer Terapan Jurnal Elementer (Elektro dan Mesin Terapan) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Communications in Science and Technology Dinamisia: Jurnal Pengabdian Kepada Masyarakat JURNAL ILMIAH INFORMATIKA ComTech: Computer, Mathematics and Engineering Applications Jurnal Pengabdian Masyarakat Multidisiplin INSERT: Information System and Emerging Technology Journal Jurnal Akuntansi Keuangan dan Bisnis Proceeding Applied Business and Engineering Conference Proceeding International Applied Business and Engineering Conference Fleksibel: Jurnal Pengabdian Kepada Masyarakat JITER-PM (Jurnal Inovasi Terapan - Pengabdian Masyarakat) Jurnal Informatika Polinema (JIP) Scientific Journal of Informatics
Kartina Diah K. W.
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Journal : JOIV : International Journal on Informatics Visualization

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Recommendation System for Mobile-Based Oil Palm Fertilization Period with Rainfall Prediction using ANN Isnaini, Mei Nanda; Sari, Juni; Kusuma Wardhani, Kartina Diah; Tri Wahyuni, Retno
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.2883

Abstract

Weather conditions significantly affect human activities, including the oil palm plantation sector, which in practice considers weather and climate conditions. Oil palm is an annual crop that requires proper nutrition throughout the year. Plant nutrition through fertilization must be according to the specific needs of oil palms. Knowing the type of fertilizer, calculating the dosage, and evaluating the climatic characteristics significantly affect the effectiveness and efficiency of fertilization. According to one palm oil farmer, fertilization should ideally be done when the soil is moist or not during the dry season so plants can absorb fertilizers properly. If fertilization is ineffective, then the operational costs of plant maintenance to buy fertilizers become less efficient. Due to climate change, farmers often find it difficult to determine the optimal timing of fertilization. Therefore, rainfall prediction is essential. Thus, fertilization can run well and get maximum results. The recommendation system in this research includes a rainfall prediction system with machine learning methods and an Artificial Neural Network. The recommendation system is a mobile-based application that allows oil palm farmers to obtain information on the appropriate time to fertilize based on rainfall. The evaluation of rainfall prediction using ANN has the MSE value of 0.0019981 and the MAPE value of 9.355%. It can be concluded that the rainfall prediction model is working optimally. This system can be combined with harvesting forecasting and recommendations of oil palm plantation periods to become a monitoring system for oil palm productivity.
Modeling and Application of Credit Scoring Based on A Multi-Objective Approach to Debtor Data in PT. Bank Riau Kepri Sugianto, -; Widyasari, Yohana Dewi Lulu; Wardhani, Kartina Diah Kusuma
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.1493

Abstract

The development of information technology in Indonesia, marked by the start of Industry 4.0, is very rapid. With the development of technology, many companies use technology to develop their business, one of which is banking, which analyses the process of prospective customers. New employees find it challenging to interpret and tend to agree more easily with prospective customers because they only see the fulfillment of general requirements. This research aims to find an overview of the primary and additional factors to analyze prospective credit customers using The Cross-Industry Standard Process for Data Mining (CRISP-DM). Develop a model in this study using data variables of prospective customers in health insurance as a moderating variable. This model tested the Decision Tree algorithm with an accuracy value of 92.49%, the Random Forest with an accuracy value of 81.72%, the Support Vector Machine (SVM) with an accuracy value of 91.25%, and K-Nearest Neighbor (K-NN) with an accuracy value. 90.58%, Gradient Boosting with an accuracy value of 90.69%, and XGBoost with an accuracy value of 93.27%. The algorithm uses a cross-validation technique at the validation stage by changing the K value to 2, 4, 6, 8, and 10. The results show that the XGBoost Algorithm accuracy is 93.27% with a K value of 8. As the highest model accuracy, this model was implemented using the XGBoost Algorithm.
Co-Authors - Sugianto Akbar, Memen Akbar, Memen Alfin H Anam, M Khairul Ardianto Wibowo Ari Azhar Cakra Ramadhana chintia romauli rambe Rambe Dewi, Meillany Dhimas Sena Rahmantara Efrizoni, Lusiana Heri Subagiyo Ike Yunia Pasa Isnaini, Mei Nanda Jan Alif K Jan Alif Kreshna Juni Nurma Sari Kamila, Aida Khairul Umam S Khairul Umam Syaliman Maksum Ro’is Adin Saf Maksum Ro’is Adin Saf Mardhiah Fadhli, Mardhiah Memen Akbar Muhammad Bambang Firdaus Muhammad mahrus zain Nurmalasari, Dini Puja Hanifah Sari, Juni Sri Devi Anjasmara Sugeng Purwantoro E.S.G.S Suhatman, Rahmat Surya, Ibnu Syaparudin BS Tisa Magrisa Tobi Arfan Tobi Arfan, Tobi Tri Wahyuni, Retno Wawan Yunanto Wenda Novayani Weslie Weslie Yohana Dewi Lulu Widyasari Yuli Fitrisia Yuliska Yuliska