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All Journal Jurnas Nasional Teknologi dan Sistem Informasi Jurnal Teknologi Informasi dan Multimedia Jurnal Abdi Insani Jurnal Sistem Komputer dan Informatika (JSON) Jurnal SASAK : Desain Visual dan Komunikasi Prosiding Konferensi Nasional PKM-CSR Bakti Sekawan : Jurnal Pengabdian Masyarakat SainsTech Innovation Journal Jurnal Elektronika dan Telekomunikasi
Kumoro, Danang Tejo
Universitas Qamarul Huda Badaruddin, Lombok Tengah
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Nursing Public Health Social Sciences Other

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COMPARATIVE ANALYSIS OF DECISION TREE AND RANDOM FOREST ALGORITHMS FOR PREDICTING DIABETES MELLITUS Desmita, Nindri Lia; Kumoro, Danang Tejo; Lonang, Syahrani
SainsTech Innovation Journal Vol. 8 No. 1 (2025): SIJ VOLUME 8 NOMOR 1 TAHUN 2025
Publisher : LPPM Universitas Qamarul Huda Badaruddin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37824/sij.v8i1.2025.783

Abstract

Diabetes mellitus (DM) is a chronic disease with an increasing number of sufferers and a risk of serious complications. Early detection is very important to prevent these risks. This study uses a public dataset from Kaggle to compare the performance of Decision Tree and Random Forest algorithms in predicting diabetes status. The dataset includes demographic and medical information such as age, hypertension, cardiac history, BMI, HbA1c, and blood glucose levels. Unbalanced data was handled using the SMOTE method, and then tested with 80:20, 70:30, and 60:40 data sharing schemes. The evaluation results showed that Random Forest excelled in all schemes, with the best performance in the 60:40 scheme (96.02% accuracy, 76.13% F1-score). This research shows that Random Forest is effective to support machine learning-based diabetes early detection system.
Model Berbasis Fuzzy Tsukamoto Untuk Perhitungan Besaran Gaji Dosen Pada Perguruan Tinggi Swasta Valian Yoga Pudya Ardhana; Eliyah A. M. Sampetoding; Danang Tejo Kumoro; Noor Alamsyah
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 3 No. 3 (2022): Maret 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v3i3.3856

Abstract

Payment of salaries is a routine activity carried out by the finance department of a company, agency or business entity. Likewise, private universities which are educational institutions have an obligation to pay the salaries of their lecturers. With the large number of lecturers and the many variables in determining the amount of lecturer salaries, it is difficult for the finance department at private universities to determine the amount of a fair salary and in other words provide a salary that is in accordance with the efforts that have been made by each lecturer. With these problems, we need a method of calculating teacher salaries. The fuzzy tsukamoto method is the most ideal method in determining the salary of lecturers at private universities because it has non-binary and non-linear problems. With the problems and solutions raised above, this study aims to provide a faster, more precise and accurate method of calculating lecturer salaries at private universities, of course with input variables that are adjusted to the rules in private universities. Salary variables, especially lecturer salary bonuses at private universities, are years of service, jafung, rank/class and the most important is the performance variable. In the performance variable, there are three specific variables, namely the tridharma of lecturers (teaching, research or publication, and community service). To achieve a bonus with many criteria, it is required that the three performance variables have many criteria, and vice versa.
Determination of the Optimum Wavelet Basis Function for Indonesian Vowel Voice Recognition Syahroni Hidayat; Habib Ratu P. N.; Danang Tejo Kumoro
Jurnal Elektronika dan Telekomunikasi Vol. 17 No. 2 (2017)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14203/jet.v17.42-47

Abstract

Nowadays, wavelet has been widely applied in extracting features of the signal for automatic speech recognition system. Wavelets have many families that are determined by their mother function and order. The use of different wavelets to analyze the same signal would bring different results. In many cases, a trial and error procedure is used to select the optimal wavelet family. That is because there are no particular wavelet basis functions that can be applied to the entire speech signals. Therefore, it is necessary to analyze the similarity between the speech signal and the wavelet base function. One of the methods that can be used is cross-correlation. In this study, the degree of correlation is determined between wavelet base function and Indonesian vowels. The influence of gender and consistencies of the results are also used in the analysis. The results show that db45 and db44 are most similar to male and female vowels utterance, respectively. For consistencies, only vowel e gives a consistent result. Overall, db44 is most similar to all Indonesian vowels utterance.
Co-Authors Adam Bachtiar Maulachela Adipta, Marazaenal Afriansyah, M. Ahmad Fatoni Dwi Putra Amir Fatah Sofyan Anggarista, Randa Ardi, Raden Fanny Printi Arief Setyanto Asno Azzawagama Firdaus Desmita, Nindri Lia Didi Putra Hariyadi Eliyah A. M. Sampetoding Eliyah Acantha Manapa Sampetoding Habib Ratu Perwira Negara Joni Saputra Lalu Jayeng rusiandi Putra Miftahul Madani Muhamad Yamin Noor Alamsyah Pratama, Ramadhana Agung Purmadi, Ary Raden Fanny Printi Ardi Susilahudin Putrawangsa Syahrani Lonang Syahroni Hidayat Syuhada, Fahmi Uswatun Hasanah Uswatun Hasanah Valian Yoga Pudya Ardhana Wibawa, Restu widya meli anggarini