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All Journal Techno.Com: Jurnal Teknologi Informasi SITEKIN: Jurnal Sains, Teknologi dan Industri Prosiding Semnastek CESS (Journal of Computer Engineering, System and Science) Jurnal CoreIT JURNAL MEDIA INFORMATIKA BUDIDARMA Seminar Nasional Teknologi Informasi Komunikasi dan Industri Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Teknologi Komputer dan Sistem Informasi Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Pendidikan dan Teknologi Indonesia KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Sains dan Informatika : Research of Science and Informatic JTKSI (Jurnal Teknologi Komputer dan Sistem Informasi)
Muhammad Irsyad
Universitas Islam Negeri Sultan Syarif Kasim Riau
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Neuroscience Transportation Other

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Journal : Jurnal Sistem Komputer dan Informatika (JSON)

 1 
Implementasi Data Mining Memprediksi Penjualan Crude Palm Oil Berdasarkan Kapasitas Tangki Menggunakan Multiple Linear Regression Ana Komaria Baskara; Alwis Nazir; Muhammad Irsyad; Yusra Yusra; Fitri Insani
Jurnal Sistem Komputer dan Informatika (JSON) Vol 4, No 3 (2023): Maret 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i3.5665

Abstract

Data mining is a process of discovering information from data that can be used to improve business, product development, and other decision-making processes. One application of data mining is in PT. Kerry Sawit Indonesia, which is an agribusiness company in the Wilmar Group that deals with processing crude palm oil (CPO). Sales of CPO are crucial for palm oil plantation companies. To increase efficiency and profitability, palm oil plantation companies can predict CPO sales to optimize sales and CPO inventory. One method that can be used to predict CPO sales is through data mining techniques. In this study, the data mining technique used is multiple linear regression. Multiple linear regression is used to determine the relationship between the tank capacity variable and CPO sales. The data used in this study are CPO production data, CPO sales data, and tank capacity data obtained from palm oil plantation companies over the last five years. The results of the Multiple Linear Regression calculation in this case study show that the coefficient of determination (R-squared) value is 0.9546, indicating that 95.46% of the CPO delivery variability can be explained by the independent variables. Additionally, the MAPE and RMSE tests show that the regression model obtained has good accuracy in predicting CPO deliveries. Therefore, this regression model can be used to predict CPO deliveries in the future, considering the predetermined independent variable values.
Implementasi Data Mining Memprediksi Penjualan Crude Palm Oil Berdasarkan Kapasitas Tangki Menggunakan Multiple Linear Regression Ana Komaria Baskara; Alwis Nazir; Muhammad Irsyad; Yusra Yusra; Fitri Insani
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 3 (2023): Maret 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i3.5665

Abstract

Data mining is a process of discovering information from data that can be used to improve business, product development, and other decision-making processes. One application of data mining is in PT. Kerry Sawit Indonesia, which is an agribusiness company in the Wilmar Group that deals with processing crude palm oil (CPO). Sales of CPO are crucial for palm oil plantation companies. To increase efficiency and profitability, palm oil plantation companies can predict CPO sales to optimize sales and CPO inventory. One method that can be used to predict CPO sales is through data mining techniques. In this study, the data mining technique used is multiple linear regression. Multiple linear regression is used to determine the relationship between the tank capacity variable and CPO sales. The data used in this study are CPO production data, CPO sales data, and tank capacity data obtained from palm oil plantation companies over the last five years. The results of the Multiple Linear Regression calculation in this case study show that the coefficient of determination (R-squared) value is 0.9546, indicating that 95.46% of the CPO delivery variability can be explained by the independent variables. Additionally, the MAPE and RMSE tests show that the regression model obtained has good accuracy in predicting CPO deliveries. Therefore, this regression model can be used to predict CPO deliveries in the future, considering the predetermined independent variable values.
Penerapan Algoritma Naïve Bayes Terhadap Klasifikasi Penerima Bantuan Program Keluarga Harapan (PKH) Amelia Irsyada; Elin Haerani; Muhammad Irsyad; Fitri Wulandari; Liza Afriyanti
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 2 (2023): Desember 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i2.7203

Abstract

Poverty in Indonesia is one of the complex social issues. As a manifestation of the government's concern about poverty in the country, various assistance programs have been established to target the impoverished population. One such program aimed at alleviating poverty in Indonesia is the Family Hope Program (Program Keluarga Harapan or PKH). PKH is a conditional cash transfer program provided to the impoverished community. The manual selection process for aid recipients is considered less than ideal, leading to issues of improper distribution. In this study, the Naïve Bayes algorithm is applied to classify PKH aid recipients in the Bungaraya Subdistrict, Siak Regency, as part of the government's efforts to tackle poverty. The dataset used consists of 560 records, including data on existing PKH aid recipients and potential recipients from various villages in the Bungaraya Subdistrict for the year 2022. The attributes considered in this research include age, income, number of dependents, dependents attending school, dependents with disabilities, housing status, floor type, and wall type. The highest accuracy obtained through calculations on Google Colab is 99% for an 80:20 ratio, while the accuracy obtained using RapidMiner is 94%.
Co-Authors Abdul Halim Afdhalel Vickro Afiana Nabilla Zulfa Ahmad Fauzan Ahyani Junia Karlina Albis Ya Albi Alwis Nazir Amelia Irsyada Ana Komaria Baskara Bebi Oktaviani Deny Dewana Hastanto Eka Pandu Cynthia Elin Haerani Elvia Budianita Endang Rasila Fahmi Kasri Febi Yanto Fina Tamima Annahwa Fitra Lestari Fitri Insani Fitri Wulandari Iis Afrianty Iwan Iskandar Jasril Jasril Lestari Handayani Liza Afriyanti Mohd. Ridho Zarkasih Rahim Muhammad Affandes Muhammad Fikry Nadila Handayani Putri Nazruddin Safaat H Nazruddin Safaat H Negara, Benny Sukma Novi Yanti Nur Habibah Lubis Pizaini Pizaini Rahma Shinta Rahmad Kurniawan Rendi Aulia Rahman Reski Mai Candra Reski Mai Candra Reski Mai Chandra Riyan Wibowo Saputra Sonya Metarice Surya Agustian Suwanto Sanjaya Teddie Darmizal Teddie Darmizal Trya Ayu Pratiwi Yusra Yusra Zul Azis Khan