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All Journal International Journal of Informatics and Communication Technology (IJ-ICT) Jurnal Informatika dan Teknik Elektro Terapan Jurnal Eksplora Informatika JURNAL PENDIDIKAN TAMBUSAI JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Jusikom: Jurnal Sistem Informasi Ilmu Komputer CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) Jifosi PLAKAT : Jurnal Pelayanan Kepada Masyarakat Jurnal Teknik Informatika (JUTIF) Jurnal Ilmiah Teknologi Informasi dan Robotika International Journal Of Computer, Network Security and Information System (IJCONSIST) Saber: Jurnal Teknik Informatika, Sains dan Ilmu Komunikasi International Journal of Information Engineering and Science
Ani Dijah Rahajoe
Bhayangkara University, Indonesia
Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences

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Journal : International Journal Of Computer, Network Security and Information System (IJCONSIST)

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Optimizing Red Onion TSS (True Shallod Seed) Production in the Lowlands Based on Smart Sensors Moeljani, Ida Retno; Rahajoe, RR Ani Dijah; N, Pangesti
IJCONSIST JOURNALS Vol 5 No 1 (2023): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v5i1.115

Abstract

Onion cultivation technology using seeds still needs to be developed and socialized at the farm level. to be socialized at the farmer level, considering that until now farmers still cultivate shallots with consumption seed bulbs because there are still not many TSS produced, especially in the lowlands.In principle, not all shallot varieties are capable of flowering, some shallot varieties are capable of flowering. flowering, some shallot varieties are only 30% capable of flowering. This problem can be solved by optimizing flowering with an automation system. The advancement of Internet of thing (IoT) technology can be applied to optimize flowering by using smart sensors on the onion. flowering by using smart sensors on several varieties of shallots. The lanchor blue variety had no flower bulbs that set fruit and produced TSS seeds. This is because all the flower bulbs of the lanchor blue variety were rotten/damaged by disease due to the use of high watering during the growth period that led to flowering, fertilization, and sprouting. There was no interaction between varieties and application of gibberellic acid + and packlobutrazol on seed yield of TSS (Table4). In Bauji and Maaserati varieties, the percentage of flower bulbs that bear fruit and seed (harvested) is still better than BiruLanchor, only about 59.68 to 70% of the total number of flower bulbs that grow (Table 4). This indicates that the process of fertilization and seed formation of shallots is not optimal.
Feature Engineering Optimization on the Performance of XGBoost, Random Forest, and Support Vector Regression Algoritms in House Price Prediction Trenggono, Brahmantio Widyo; Diyasa, I Gede Susrama Mas; Rahajoe, Ani Dijah
IJCONSIST JOURNALS Vol 6 No 1 (2024): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v6i1.149

Abstract

As the years go by, the ever-increasing movement of house prices has become an important factor in investment decisions and financial planning to curb inflation. However, fluctuations or increases in house prices can be caused by various factors that can affect the value of house price predictions. This study aims to analyze the influence of optimization and the relationship between feature engineering and modeling in house price predictions. The research stages include data preprocessing, logarithmic transformation, feature engineering, data splitting, and optimization in determining parameters during tuning. Model performance is evaluated using the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and Determination coefficient (R-Squared) metrics. The results show that the Support Vector Regression algorithm produces the best performance with a MAE value of 274 million, an RMSE of 780 million, a MAPE of 7%, and an R-Squared of 98%. This research is expected to serve as a reference for future studies on regression model optimization, particularly in decision-making for more accurate house price predictions.
Co-Authors Agung Subekti, Mohamad Rafli Ainur Rahim Al Afgany, Muhammad Iqbal Andre Leto Anna Fauziah arif arizal Arif Setyo Wibowo Chaurina, Agfanadita Rezkia Daniar, Aninditya Dian Agus Prawinata Diyasa, I Gede Susrama Mas Eka Prakarsa Mandyartha Feriza, Reyana Dinda Maulan Fransiska, Amelia Henni Endah Wahanani I Gede Susrama Mas Diyasa Ida Retno Moeljani Indartono, Taqiyya Jr, Angelo A. Beltran M Mahaputra M. Mahaputra Made Hanindia Prami Swari Megantara, Sofia Ramadhani Muhammad Farhan Maulana Muttaqin, Faisal N, Pangesti Nurlaili, Afina Lina P. Eko Prasetyo Perkasa, Laurensius Gading Surya Pramnesti, Adisty Regina Retno Mumpuni Reza Aminullah Safeyah, Muchlisiniyati Sanjaya, Yushinta Aristina Satrio Budi Wahyuono Soffiana Agustin, Soffiana Subekti, Mohamad Rafli Agung Suriansyah, Muhammad Suryantari, Putu Anggi Syariful Alim Trenggono, Brahmantio Widyo Waskito, Muhammad Rizal Winarko, Edi Zakaria, Rino