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All Journal Journal of Computer System and Informatics (JoSYC) EKONOMI, KEUANGAN, INVESTASI DAN SYARIAH (EKUITAS) Bulletin of Computer Science Research
Irma Hakim
Universitas Muhammadiyah Makassar, Makassar
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Industrial & Manufacturing Engineering

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Pemetaan Produksi Tanaman Tomat di Indonesia Berdasarkan Provinsi Menggunakan Algoritma K-Means Clustering Syaifuddin Syaifuddin; Ramlah Ramlah; Irma Hakim; Yunida Berliana; Nurhayati Nurhayati
Journal of Computer System and Informatics (JoSYC) Vol 3 No 4 (2022): August 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v3i4.2206

Abstract

Tomato is one of the essential horticultural commodity vegetables because it has high economic value. The need for this plant continues to increase along with the increase in population, income levels, and heightened public awareness of the importance of nutritional value. Therefore, this research aims to see and map the production of tomato plants in Indonesia by the province in the form of clusters (grouping). The research data used in this paper is data on tomato production in Indonesia by the province in the last five years (2017-2021) obtained from the District/City Agriculture Service of each province and the Indonesian Central Statistics Agency. The algorithm proposed in this study is K-Means Clustering with the help of RapidMiner. The results of the proposed paper are grouping and mapping of tomato production in Indonesia, which is divided into 5 (five) zones, including the Black Zone (areas with very high tomato production), which consists of 1 province, Green Zone (areas with high production of tomatoes). Which consists of 2 provinces, the Blue Zone (areas with moderate production), which consists of 4 provinces. The Light Blue Zone (areas with low production), which consists of 8 provinces, and the Orange Zone (areas with moderately low production), which consists of 18 provinces.
Analisis Penerapan Preventive Maintenance Terhadap Peningkatan Produktivitas Produksi Ahmad Jibril; Dimas Bayu Sasongko; Warkianto Widjaja; Irma Hakim; Didit Hadayanti
Ekonomi, Keuangan, Investasi dan Syariah (EKUITAS) Vol 4 No 4 (2023): May 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/ekuitas.v4i4.3401

Abstract

This study aims to analyze the effect of implementing preventive maintenance on increasing production productivity in a fish canning company in East Java. This study uses a quantitative method, namely the method used to examine certain populations or samples with data collection techniques carried out through observation and the results of questionnaire answers that have been filled out by employees in the production and engineering departments. The population used in this study is the production machines used to carry out the production process with the samples used, namely production machines of 30 units. Data analysis in this study used SPSS software by conducting hypothesis testing which consisted of a coefficient of determination test (R-square) and a partial t test. Based on the results of the analysis of the calculation of the hypothesis, the t-count value of 10,624 is greater than the t-table value, which is 1,701 and the significance value obtained is less than 0.05, which means that the application of preventive maintenance on production machines has a positive and significant effect on increasing production productivity, while the R-square value obtained is 0.885 which states that the variable of applying preventive maintenance has an effect on increasing production productivity by 88.50% and the remaining 11.50% is influenced by other variables.
Integrasi PIPRECIA dan MACROS Dalam Penentuan Lokasi Pertanian Berkelanjutan Irma Hakim; Asdi Asdi; Oktoni Eryanto
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.703

Abstract

This study develops a decision support system for selecting sustainable agricultural locations amid the challenges of climate change. Two integrated multi-criteria decision-making (MCDM) methods are employed: PIPRECIA (Pivot Pairwise Relative Criteria Importance Assessment) to determine the importance weights of criteria based on expert rankings, and MACROS (Measurement Alternatives and Ranking according to the Compromise Solution), a structured method used to evaluate and rank alternatives through normalization, weighted scoring, and compromise-based ranking. Six key criteria are considered: water availability, land suitability, flood risk, market access, government support, and microclimate conditions. The integration of PIPRECIA and MACROS enables a systematic and transparent evaluation process. The results indicate that location "G" achieves the highest compromise score of 0.985, signifying its suitability as the most optimal site for sustainable agricultural development. The primary contribution of this research lies in offering a quantitative and structured approach that accommodates environmental uncertainties while enhancing decision-making transparency. By integrating expert judgment with computational assessment, this model supports data-driven decision-making in the planning of agricultural development. These findings are expected to provide strategic insights for policymakers in formulating adaptive agricultural policies, strengthening food security, and improving farmer welfare through accurate and sustainable location selection.
Co-Authors Ahmad Jibril Asdi Asdi Didit Hadayanti Dimas Bayu Sasongko Nurhayati Nurhayati Oktoni Eryanto Ramlah Ramlah Syaifuddin Syaifuddin Warkianto Widjaja Yunida Berliana