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All Journal Jurnal Transformatika JIEET (Journal of Information Engineering and Educational Technology) Jurnal Pengabdian Masyarakat Router : Jurnal Teknik Informatika dan Terapan Julia Jurnal
Andri Triyono
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Humanities Computer Science & IT Education Engineering Languange, Linguistic, Communication & Media Public Health Other

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ALGORITMA RANDOM FOREST, DECISION TREE, DAN XGBOOST UNTUK KLASIFIKASI STUNTING PADA BALITA Dhika Malita; DHIKA MALITA PUSPITA ARUM; KARTIKA IMAM SANTOSO; ANDRI TRIYONO; EKO SUPRIYADI; AGUS SUSILO NUGROHO; Widodo, Edi
Jurnal Transformatika Vol. 23 No. 1 (2025): July 2025
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v23i1.12202

Abstract

At the age of toddlers, children need special attention because their brains develop around 80%. Stunting is a form of long-term nutritional deficiency that occurs during the growth and development of children, which are marked with height that is not appropriate or less compared to children their age based on the standard WHO. This condition can adversely affect the cognitive development and health of children. Identifying toddlers who are at risk of experiencing stunting at an early stage is very important to reduce the adverse effects that can affect their quality of life in the future. Traditional methods are less effective in predicting stunting because they often ignore the complex factors that affect the nutritional status of toddlers. This study aims to classify stunting toddlers using Random Forest, Decision Tree, and Extreme Gradient Boost (XGBOOST) algorithms. The results obtained showed that the accuracy of the Random Forest algorithm received the highest accuracy of 99.72 %, Extreme Gradient Boost (XGBOOST) at 99.58 %, and Decision Tree received 98 87 %accuracy.
Implementasi Penerangan Jalan Berbasis Panel Surya Pada Desa Tunggak Toroh Grobogan Susilo Nugroho, Agus; Mika Agustiana; Andri Triyono; Dhika Malita Puspita Arum; Eko Supriyadi
Jurnal Pengabdian Masyarakat - PIMAS Vol. 3 No. 1 (2024): Februari
Publisher : LPPM Universitas Harapan Bangsa Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/pimas.v3i1.1373

Abstract

Jalan merupakan sebuah infrastruktur utama penunjang kehidupan manusia. Ketika infrastruktur berupa jalan itu sudah baik, maka segala aktifitas masyarakat, mulai dari perekonomian, transportasi, hingga pemerataan pembangunan dapat terwujud pula dengan baik. Desa Tunggak, Kecamatan Toroh, Kabupaten Grobogan merupakan salah satu desa di Jawa Tengah yang infrastruktur jalannya sudah cukup memadai. Namun ada sebuah jalan yang belum memiliki penerangan maksimal di malam hari. Selain visibilitas yang tidak baik dan meningkatkan resiko kecelakaan, juga beresiko mengundang kejahatan. Karenanya, anggota KKN Universitas An Nuur 2023 membuat lampu penerangan jalan di Desa Tunggak. Lampu penerangan jalan dibuat dengan tenaga surya. Dipilihnya lampu penerangan jalan bertenaga surya ini guna memaksimalkan efisiensi daya. Metode yang digunakan dalam kegiatan tersebut adalah identifikasi, implementasi, serta capaian atau luaran kegiatan. Masyarakat Desa Tunggak sangat mengapresiasi pembuatan lampu panel surya yang dilakukan tim KKN Universitas An Nuur 2023. Aktifitas masyarakat di malam hari ketika melewati jalan yang sudah ada panel suryanya, menjadi lebih maksimal dan produktif.
PREDIKSI TINGKAT KELULUSAN PESERTA DIDIK SMK FATHUL ULUM GABUS DENGAN METODE NAIVE BAYES Wahyudi; Eko Supriyadi; Andri Triyono
Julia: Jurnal Ilmu Komputer An Nuur Vol 5 No 1 (2025): Julia Jurnal
Publisher : LPPM Universitas An Nuur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35720/julia.v5i1.3

Abstract

The graduation of students refers to those who are able to complete and meet the graduation requirements set through a graduation meeting based on the decision letter signed by the school principal. Graduation rate data can be used to help make policies and strategies for the school to improve graduation rates in the following year. This study utilizes classification or prediction methods to analyze the graduation rates of students at SMK Fathul Ulum Gabus. The method used in this study is Naive Bayes, using variables such as practical exam scores, school exam scores, competency test scores, student attendance, and student behavior. The purpose of this study is to test the accuracy of the Naive Bayes method in predicting graduation rates based on data collected from 2019 to 2024. The research process includes data collection, data integration, and model training using Naive Bayes, which produces fairly accurate predictions with an accuracy of 94.64%. Based on this accuracy, it can be concluded that the Naive Bayes method can be used to predict graduation rates at SMK Fathul Ulum Gabus.
COMPARISON OF SVM, KNN, AND NAIVE BAYES METHOD WITH N-GRAM IN TRAFFIC ACCIDENT CLASSIFICATION Dhika Malita Puspita Arum; Andri Triyono
Julia: Jurnal Ilmu Komputer An Nuur Vol 1 No 01 (2021): Julia Jurnal
Publisher : LPPM Universitas An Nuur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35720/julia.v1i01.11

Abstract

Traffic accidents that occur in Indonesia are still relatively high, the information can be easily obtained through social media, one of which is Twitter. The amount of traffic accident information can be processed and classified according to certain categories. Traffic accident data classification is done using SVM, KNN and Naïve Bayes methods using n-gram feature extraction. The results of this study indicate the best accuracy is 87.63 using the KNN method.
EARLY DETECTION OF DIABETES MELLITUS USING RANDOM FOREST ALGORITHM Andri Triyono; Rahmawan Bagus Trianto; Dhika Malita Puspita Arum
Julia: Jurnal Ilmu Komputer An Nuur Vol 1 No 01 (2021): Julia Jurnal
Publisher : LPPM Universitas An Nuur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35720/julia.v1i01.13

Abstract

Diabetes mellitus is a deadly disease. Patients with this disease often do not realize that they are improving their diabetes mellitus. It is necessary to do early prevention in order to reduce the sudden death rate of people with diabetes mellitus. In addition, during the COVID-19 pandemic, which increases the risk of death for people with comorbid diabetes mellitus. A system model for the prediction of diabetes mellitus is needed for early diagnosis of this disease. By using machine learning techniques using the Random Forest algorithm and Information Gain can be used to predict diabetes mellitus. This model has a fairly high level of accuracy, which is 98.27%, precision is 97.69% and recall is 98%. 
GENETIC ALGORITHM FOR FEATURE SELECTION IN NAÏVE BAYES IN LIFE RESISTANCE CLASSIFICATION ON BREAST CANCER PATIENT Dhika Malita Puspita Arum; Andri Triyono
Julia: Jurnal Ilmu Komputer An Nuur Vol 1 No 01 (2021): Julia Jurnal
Publisher : LPPM Universitas An Nuur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35720/julia.v1i01.14

Abstract

Breast cancer is the most common cancer in women's suffering and is the second leading cause of death for women (after lung cancer). More than one million cases and nearly 600,000 breast cancer deaths occur worldwide each year. Survival is generally defined as surviving patients over a period of time after the diagnosis of the disease. Accurate predictions about the likelihood of survival of breast cancer patients can allow doctors and healthcare providers to make more informed decisions about patient care. To classify the survival of breast cancer patients can do the utilization of data mining techniques with Naive Bayes algorithm. Naive Bayes is very simple and efficient but very sensitive to the features so from it the selection of the appropriate features is in need because irrelevant features can reduce the level of accuracy. Naive Bayes will work more effectively when combined with some attribute selection procedures such as Genetic Algorithm. In this study the researchers proposed the Genetic Algorithm for Feature Selection on Naive Bayes so as to improve the accuracy of breast cancer survival classification results. In this study using a private dataset breast cancer patients. The results show that Naive Bayes Genetic Algorithm has a higher accuracy of 90% compared to Naive Bayes with 86% accuracy 
PENGGUNAAN ALGORITMA FP-GROWTH UNTUK MENENTUKAN PAKET PENJUALAN PADA TOKO PERLENGKAPAN KONVEKSI SRI BUSANA Andri Triyono; Dhika Malita Puspita Arum; Rahmawan Bagus Trianto
Julia: Jurnal Ilmu Komputer An Nuur Vol 2 No 01 (2022): Julia Jurnal
Publisher : LPPM Universitas An Nuur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35720/julia.v2i01.16

Abstract

Consumers of the Sri Busana convection shop are mostly tailors, both home and convection tailors, which are pretty large, especially in Grobogan district. The increasing number of fashion businesses or tailors in Grobogan district makes data on goods and sales at the sri busana convection shop increase because the sri busana convection shop always strives to meet the needs of tailors or home convection. In overcoming the problem of finding more efficient consumer patterns, an analysis of buying patterns is carried out. Consumer buying patterns were analyzed using Association rules and FP-Growth methods. With this algorithm, the process of determining consumer purchasing patterns consists of 2 product combinations with a support value of 50% and a confidence value of 100%. 3 product combinations with a support value of 40% and a confidence value of 80%. 4 product combinations with a support value of 40% and a confidence value of 80%. 
OPTIMIZATION OF PARTICLE SWARM OPTIMIZATION IN NAÏVE BAYES FOR CAESAREAN BIRTH PREDICTION Dhika Malita Puspita Arum; Andri Triyono; Eko Supriyadi; Rahmawan Bagus Trianto
Julia: Jurnal Ilmu Komputer An Nuur Vol 2 No 01 (2022): Julia Jurnal
Publisher : LPPM Universitas An Nuur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35720/julia.v2i01.17

Abstract

The Maternal Mortality Rate (MMR) in 2017 according to the World Health Organization (WHO) is estimated to reach 296,000 women who die during and after pregnancy or childbirth. Caesarean birth is the last alternative in labor if the mother cannot give birth normally due to certain indications with a high risk, both for the mother and the baby. factors of a mother giving birth by caesarean section, such as placenta previa, hypertension, breech baby, fetal distress, narrow hips, and can also experience bleeding in the mother before the delivery stage. It is hoped that delivery by caesarean method can minimize problems for the baby and mother. Accurate prediction of the condition of the mother's pregnancy can enable d octors, health care providers and mothers to make more informed decisions regarding the management of childbirth. To predict caesarean births, data mining techniques using the Naive Bayes algorithm can be used. Naive Bayes is very simple and efficient but very sensitive to features, therefore the selection of appropriate features is very necessary because irrelevant features can reduce the level of accuracy. Naive Bayes will work more effectively when combined with several attribute selection procedures such as Particle Swarm Optimization. In this study, the researcher proposes a Particle Swarm Optimization algorithm for attribute weighting in Naive Bayes so as to increase the accuracy of Caesarean birth prediction results 
PENERAPAN SISTEM INFORMASI STOK BARANG BERBASIS APLIKASI UNTUK MENINGKATKAN EFISIENSI PENGELOLAAN INVENTARIS PADA TOKO SEMBAKO Agus Condro Wibowo; Dwi Kurniawan Aprilianto; Ahmad Yususf Mufarihin; Andri Triyono; Dhika Malita Puspita Arum
Julia: Jurnal Ilmu Komputer An Nuur Vol 5 No 1 (2025): Julia Jurnal
Publisher : LPPM Universitas An Nuur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35720/julia.v5i1.22

Abstract

Perkembangan teknologi informasi yang berkembang pesat memberikan dampak yang signifikan ke berbagai sektor, seperti pengelolaan inventaris di sektor ritel. penelitian ini bertujuan untuk menerapkan sistem informasi yang berbasis aplikasi untuk stock barang agar meningkatkan efisiensi pengelolaan inventaris pada toko. aplikasi yang di bangun di harapkan dapat mempermudah pemantauan stok barang, mempercepat proses pencatatan transaksi, serta meminimalisir kesalahan dalam mengelola data stock. metode yyang di gunakan dalam penelitian ini adalah pengembangan aplikasi berbasis perangkat lunak yang dapat memberikan hasil laporan inventaris secara akurat. hasil yang di harapkan dari penerapan sistem informasi ini adalah pengurangan tingkat kesalahan, serta pengelolaan stok yang lebih mudah dan cepat. Penelitian ini memiliki kontribusi dalam memberikan solusi bagi toko-toko untuk menghadapi tantangan pengelolaan inventaris yang lebih kompleks di era digital sekarang. 
PREDIKSI LUAS PANEN DI KECAMATAN PURWOADADI MENGGUNAKAN ALGORITMA REGRESI LINEAR BERGANDA Muhammad Akbar Mustofa; Andri Triyono; Agus Susilo Nugroho
Julia: Jurnal Ilmu Komputer An Nuur Vol 5 No 1 (2025): Julia Jurnal
Publisher : LPPM Universitas An Nuur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35720/julia.v5i1.23

Abstract

Agriculture, particularly rice cultivation, is highly vulnerable to climate change because it depends on water cycles and weather conditions to maintain productivity. Climate change affects crop growth, development, and yields, as agricultural activities are heavily dependent on weather and climate. This study utilizes data mining to introduce a new breakthrough in addressing rice farming issues in Grobogan Regency, Purwodadi District. The method used is multiple linear regression, with the dependent variable being harvested area and the independent variables including plxanted area and rainfall. The objective of this research is to test and develop data mining methods to predict yield levels, thereby assisting local governments in decision-making during crop failures, based on agricultural data from 20192023. The research process involves data collection, preprocessing, algorithm implementation, and result evaluation. The analysis shows that the multiple linear regression model provides reasonably accurate predictions, with a Root Mean Square Error (RMSE) value of 209.042 and a Relative Root Squared Error (RRSE) of 0.111. Furthermore, the analysis reveals that planted area significantly influence the harvested area. These findings offer insights for local governments as policymakers in providing aid during crop failures. 
Co-Authors Agus Condro Wibowo Agus Susilo Nugroho Ahmad Yususf Mufarihin Alfianaa khanifiyah Arum, Dhika Malita Puspita Dhika Malita Dhika Malita Dhika Malita Puspita Dwi Kurniawan Aprilianto Edi Widodo, Edi Eko Supriyadi Elsa Syahriza Putri Kartika Imam Santoso Mika Agustiana Muhamad nuryahya Muhammad Akbar Mustofa Rahmawan Bagus Trianto susilo nugroho, agus WAHYUDI