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All Journal TeknoIS : Jurnal Ilmiah Teknologi Informasi dan Sains
Irmayansyah Irmayansyah
Sekolah Tinggi Ilmu Komputer Binaniaga. [https://orcid.org/0000-0003-2410-5299]
Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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Penerapan Metode Fuzzy Tsukamoto untuk Prediksi Jumlah Produksi Tanaman Cabai Irmayansyah Irmayansyah; Annisa Nur Rossdiana
Teknois : Jurnal Ilmiah Teknologi Informasi dan Sains Vol 11, No 1 (2021)
Publisher : Sekolah Tinggi Ilmu Komputer Binaniaga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36350/jbs.v11i1.98

Abstract

Inaccuracy in predicting the amount of chili production is one of the problems found in chili production. Based on the data obtained from observations and interviews conducted with the supervisor of chili plantations, there are 3 variables that can be used to predict the amount of chili production, namely land area, number of seeds, and maintenance costs. The application of the Fuzzy Tsukamoto method to this problem is one of the most appropriate and effective ways. To find out the accuracy of the application of the Tsukamoto fuzzy method for predicting the amount of chili production, it was carried out by comparing the prediction of the amount of chili production obtained from the application of the Fuzzy Tsukamoto method with the actual data on the amount of chili production using the Mean Absoulute Percentage Error (MAPE) formulation, the results obtained were 14%. Can be categorized as a good forecast. The existence of a system for predicting the amount of chili production will be more effective and efficient in its use, the system testing was carried out on 30 users using the Post-study System Usability Quistionnaire (PSSUQ)instrument, it was obtained a value of 80,15% was obtained which can be categorized as feasible to be implemented.
Penerapan Metode Algoritma C4.5 Untuk Prediksi Mahaiswa Non Aktif Irmayansyah Irmayansyah; Erisya Lastrini
Teknois : Jurnal Ilmiah Teknologi Informasi dan Sains Vol 11, No 2 (2021): November
Publisher : Sekolah Tinggi Ilmu Komputer Binaniaga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36350/jbs.v11i2.119

Abstract

The problem of non-active students is something to be aware of because it can affect the quality of education and result in a decrease in campus financial income. If the problem of inactive students can be predicted faster, then the management can prevent and anticipate early. To solve the problem, c4.5 algorithm is applied to the prediction of non-active students in order to produce patterns based on classification results. By using IPS, Attendance, Annual Income, cost sources, and payment status. This is done to monitor students who are potentially non- active so as to anticipating for the decline of active students. In this study, feasibility test has been conducted, with a feasibility value of 87.50%, and also has been conducted accuracy test using confussion matrix formula with 81% accuracy result.
Penerapan Algoritma K-Means Untuk Pemetaan Potensi Calon Mahasiswa Baru Irmayansyah Irmayansyah; Syafi'i Eko Triyono
TeknoIS : Jurnal Ilmiah Teknologi Informasi dan Sains Vol 12, No 2 (2022): Volume 12, No 2 (2022): July
Publisher : Universitas Binaniaga Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36350/jbs.v12i2.139

Abstract

The process of mapping the potential of prospective new students is a grouping of prospective students based on various criteria which will be grouped based on their potential, both low and high, in order to assist the PR/promotion bureau in providing reference data and information in order to determine what promotion strategy will be carried out in the next period. This research can provide a mapping of potential new students using the K-Means Clustering Algorithm, namely by analyzing the initial data group, transforming the initial data and performing grouping calculations, after that the results of the grouping calculations can be re-analyzed to see where the school came from, the location of the school and the location of the school. the origin of information for each member in each group. In it applied the variables, namely the origin of the school, the location of the origin of the school and the origin of the information. This is done to map the potential of prospective new students, in order to assist the public relations/promotion bureau in providing reference data and information in order to determine what promotion strategy will be carried out in the next period. A cluster validity test has been carried out using the Silhoutte Coefficiency of the K-Means algorithm which is applied with a value of 0.6113 which means that the cluster created is included in the "Medium Structure" category, it is based on the Sillhoutte Category table according to Kauffman and Roussseeuw.
Penerapan Algoritma Naïve Bayes Untuk Penentuan Diagnosa Obesitas Pada Peserta Sosialisasi Deteksi Dini Penyakit Tidak Menular (PTM) Ida Maryani; Irmayansyah Irmayansyah
TeknoIS : Jurnal Ilmiah Teknologi Informasi dan Sains Vol 13, No 2 (2023): July
Publisher : Universitas Binaniaga Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36350/jbs.v13i2.200

Abstract

Obesity is excess fat accumulation due to an imbalance between energy intake and energy expenditure for a long time. Obesity can cause various non-communicable diseases, including heart disease, stroke, diabetes, high blood pressure, gallstones, respiratory problems, cancer, osteoarthritis, including infertility. The Regional Government through Posbindu Non-Communicable Diseases (PTM) in every UPTD Puskesmas carries out socialization as an early detection of PTM risk. This activity is carried out every two or three months, by conducting routine health checks for employees. Measurement of obesity diagnosis is usually by using the Body Mass Index (BMI). BMI is a measurement method by calculating the height in meters and weight in kilograms. However, BMI also has drawbacks, namely it cannot distinguish between muscle mass and fat mass. Apart from BMI, other factors also affect the diagnosis of obesity, including gender, age, abdominal circumference, risky behaviors such as lack of physical activity, eating patterns of excess sugar, excess salt, excess fat, not eating enough fruits and vegetables. With so many determinants of the diagnosis of obesity, the Naïve Bayes Algorithm is used to predict the diagnosis of obesity more effectively and accurately. The application built is in the form of a prototype that utilizes the PHP programming language. This study used data from the health examination of participants in the socialization of PTM risk early detection with a total of 60 participants. There are 10 variables used, namely Gender, Age, Height, Weight, Abdominal Circumference, Lack of Physical Activity, Eating Patterns of Excess Sugar, Excess Salt, Excess Fat and Undereating and Fruit while there are 2 classes, namely obesity and normal diagnoses. Based on the results of calculating accuracy with the Confusion Matrix, an accuracy value of 86.6% is obtained.
Co-Authors Annisa Nur Rossdiana Erisya Lastrini Ida Maryani Syafi'i Eko Triyono