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All Journal Infotekmesin International Journal Of Science, Technology & Management (IJSTM) Journal of Technology and Informatics (JoTI)
Eko Nugroho, Wildani
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering Transportation

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Penerapan Algoritma K-Means Untuk Mengelompokkan Makanan Berdasarkan Nilai Nutrisi Alga Vredizon, Prayoga; Firmansyah, Hasbi; Shafira Salsabila, Nadya; Eko Nugroho, Wildani
Journal of Technology and Informatics (JoTI) Vol. 5 No. 2 (2024): Vol. 5 No.2 (2024)
Publisher : Universitas Dinamika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37802/joti.v5i2.577

Abstract

Penelitian ini bertujuan untuk mengelompokkan makanan yang memiliki nilai nutrisi yang serupa. Yang mana makanan dibagi ke dalam 3 cluster yaitu makanan yang mempunyai kadar nutrisi tinggi, sedang dan rendah. Hasil pengelompokan pada penelitian ini dapat digunakan untuk pemilihan dan konsumsi makanan dalam pemenuhan nutrisi dan juga dapat digunakan untuk mencegah timbulnya penyakit yang disebabkan oleh makanan. Seperti makanan pada cluster 0 bisa dipilih jika ingin menaikkan berat badan. Makanan cluster 1 dapat menjadi patokan jika dikonsumsi terlalu banyak dapat menyebabkan obesitas dan cluster 2 dapat dipilih jika ingin melakukan diet atau menurunkan berat badan. Hasil ini ditunjukkan dari hasil klasterisasi di mana cluster pertama diisi oleh makanan dengan kadar kalori dan protein yang cukup tinggi dan kadar lemak, karbohidrat yang rendah. Cluster kedua diisi oleh makanan dengan kadar kalori, protein dan lemak yang tinggi serta kadar karbohidrat yang rendah. Cluster ketiga diisi oleh makanan dengan kadar kalori, protein, lemak dan karbohidrat yang rendah. Penelitian ini menggunakan metode clustering dengan menerapkan algoritma K-Means karena efektif dalam melakukan klasterisasi terhadap tipe data numerik dan menguji menggunakan Elbow Method dan Davies Bouldin Index.
Narrowband IoT in Livestock Farming: A Technological Innovation for Productivity and Sustainability Sutanto, Achmad; Rakhman, Arif; Afriliana, Ida; Hernowo, Rudi; Eko Nugroho, Wildani; Fayruz, Mohammad
International Journal of Science, Technology & Management Vol. 5 No. 5 (2024): September 2024
Publisher : Publisher Cv. Inara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46729/ijstm.v5i5.1030

Abstract

The integration of technology in livestock farming is crucial for enhancing production efficiency and animal welfare. This study aimed to develop and evaluate the implementation of a Narrowband IoT (NB-IoT)-based automated monitoring system in poultry farming. Using an experimental design, the research involved 30,000 day-old chicks at PT. Anugerah Teknologi Ternak in Central Java, Indonesia. The NB-IoT system collected real-time data on environmental parameters and poultry activity. Time-series analysis revealed non-stationary data, while correlation analysis showed a strong negative relationship between temperature and humidity (r = -0.8521). Anomaly detection identified 13.33% of observations as anomalous, demonstrating the system's capability for early issue detection. Regression modeling (R-squared = 0.7261) indicated that temperature and humidity significantly influence poultry productivity. The study concludes that NB-IoT implementation in poultry farming has significant potential for enhancing productivity through real-time monitoring and early anomaly detection, supporting more efficient and sustainable precision farming practices. However, limitations in data stationarity and sample generalizability suggest the need for further research to improve long-term predictions and broaden applicability across diverse farming contexts.
K-Means And K-Medoids Algorithms For Food Clusterization Optimized By Nutritional Value Eko Nugroho, Wildani; Dwi Kurniawan, Safar; Alga Vredison, Prayoga; Firmansyah, Hasbi
International Journal of Science, Technology & Management Vol. 6 No. 4 (2025): July 2025
Publisher : Publisher Cv. Inara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46729/ijstm.v6i4.1327

Abstract

This study examines the impact of flexible work arrangements on productivity in start-up companies located in Bandung Techno Park, using work-life balance as a mediating variable. With a sample size of 168 start-up company members in Bandung Techno Park (BTP), this study uses a probability sampling technique. This study uses quantitative research techniques and structural equal modeling (SEM) based on partial least squares (PLS-SEM) for data analysis. Based on the results of the study, work-life balance and increased productivity are greatly enhanced by flexible work arrangements. The mediating variable of work-life balance acts as a mediator between productivity and flexible work schedules. The findings of this study can be a guide for business management who want to increase the productivity of start-up companies through work arrangement solutions that support work-life balance.
Metode Naive Bayes Dalam Menentukan Program Studi Bagi Calon Mahasiswa Baru Eko Nugroho, Wildani; Sofyan, Ali; Somantri, Oman
Infotekmesin Vol 12 No 1 (2021): Infotekmesin: Januari 2021
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v12i1.491

Abstract

In a university, determining a study program for prospective students is something that is often done to focus on prospective students so that they are in accordance with their competencies. This is a very important hope, because prospective students can develop self-competence according to their academic abilities. This research method uses several stages, including data cleaning, data collection, determining criteria, determining probability, and final testing. The Naïve Bayes method with a case study at the Private Madrasah Aliyah PAB 6 Helvetia and testing of 100 student data with an accuracy rate of 90% is a previous research. The purpose of this study was to make a classification of majors based on the criteria, while in this study the aim of making a classification of study programs for prospective new students. In this study, the same method was used but the number of data records was different, the test data was 1671 student data records, the data was obtained from 2256 data records.From the total data records were 2256, after data cleaning and data collection were carried out, 1671 test data were obtained. In the test data, there are several probability values that contain various criteria and attributes used to determine the classification of study programs for prospective new students. The number of data records is divided into 2 parts, the first is used for training data with 1158 data with a percentage of 70%, and testing data with 513 data records with a percentage of 30%. From the test results with the same method with different number of data records, the accuracy rate is from 90% to 96% with an accuracy value of 96.68%. From this accuracy value shows that the classification results obtained show the Pharmacy DIII study program.
Optimalisasi Metode Naive Bayes untuk Menentukan Program Studi bagi Calon Mahasiswa Baru dengan Pendekatan Unsupervised Discretization Eko Nugroho, Wildani; Prihandoyo, Teguh; Somantri, Oman
Infotekmesin Vol 13 No 1 (2022): Infotekmesin: Januari, 2022
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v13i1.1048

Abstract

The admission of prospective new students must consider various procedures to direct prospective new students in determining the study program they are interested in. This study will discuss the optimization of the Naive Bayes method to determine the study program or major for prospective new students with the Unsupervised Discritization method approach. There are several stages of research methods carried out in this study, including Data Cleaning, Data Collection, Criteria Determination, Probability Determination, and Data Testing. This research has been carried out using the same method, namely the Naïve Bayes method which is used to classify the interests of prospective new students in determining the study program with an accuracy value of 96.68%. Ongoing research uses the same method, namely Naive Bayes, then optimization is carried out with the Unsupervised Discretization method approach. For data testing, there are 1671 student data records. After testing with the same method and optimizing it, the accuracy value from 96.68% became 97.66% with the classification results showing the DIII Pharmacy study program. The purpose of this research is to produce a classification in determining the study program or major for prospective new students using the Naïve Bayes method by the optimization of the Unsupervised Discretization method. From the results of testing the data, the Naïve Bayes method after optimization with the Unsupervised Discretization method is very good compared to the application before optimization.
Co-Authors Achmad Sutanto Alga Vredison, Prayoga Alga Vredizon, Prayoga Arif Rakhman Firmansyah, Hasbi Hernowo, Rudi Ida Afriliana Mohammad Fayruz Oman Somantri Prihandoyo, Teguh Safar Dwi Kurniawan Shafira Salsabila, Nadya Sofyan, Ali