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Syahrani Lonang
Universitas Qamarul Huda Badaruddin
Religion Agriculture, Biological Sciences & Forestry Arts Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences

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Klasifikasi Status Stunting Pada Balita Menggunakan K-Nearest Neighbor Dengan Feature Selection Backward Elimination Lonang, Syahrani; Normawati, Dwi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 1 (2022): Januari 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i1.3312

Abstract

The main problem regarding nutrition faced by Indonesia is stunting, where Indonesia is ranked fifth in the world with the largest stunting prevalence rate in 2017, which is 29.6% of all Indonesian children. Stunting is a child under five years who has a z-score value of less than -3 standard deviations (SD). Stunting has a negative impact, namely it can disrupt the physical and intellectual development of toddlers in the future. In this case, the examination of stunting status by medical personnel is still carried out manually which takes a long time and is prone to inaccuracies. This study aims to classify stunting status in toddlers by applying the K-Nearest Neighbor method using the Backward Elimination feature selection to get fast and accurate results. Based on the results of this study, the average accuracy produced by the K-Nearest Neighbor algorithm at k=5 is 91.90% with 9 attributes and the average accuracy produced by the K-Nearest Neighbor algorithm with the addition of Backward Elimination is 92.20%. with 8 attributes. These results indicate that the application of Backward Elimination can increase the accuracy value of the K-Nearest Neighbor algorithm and also perform attribute selection.
Training on how to use Social Media Wisely and Ethically Herman Herman; Imam Riadi; Dikky Praseptian M; Faiz Isnan Abdurrachman; Syahrani Lonang
ABDIMAS: Jurnal Pengabdian Masyarakat Vol. 5 No. 2 (2022): ABDIMAS UMTAS: Jurnal Pengabdian Kepada Masyarakat
Publisher : LPPM Universitas Muhammadiyah Tasikmalaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (803.308 KB) | DOI: 10.35568/abdimas.v5i2.2686

Abstract

Nowadays, the behavior of users in social media arguably represent human behavior in the real world. Training on how to use social media wisely and ethically to young ages is needed to grow the good behavior. Based on a preliminary study, the ages of students in SMK Kesehatan Binatama is considered a terget of such training. 15 years old dominates with 63.6% followed by 16 years old with 29.3% according to age which has the highest penetration rate of social media users reaching 99.16%, namely ages 13-18 years. The activeness of students in social media reaches 99%. The number of hours students use social media where 10.1 % stated between 0-2 hours, 40.4% stated 2-5 hours, 36.4% stated 5-10 hours and 11.1 % more than 10 hours. Knowledge training on social media has been carried out several times but must continue to be carried out along with the development of social media technology and the shift in the age of its users. Training activities with wise and ethical materials using social media have been successfully held with the expected results. Participants' knowledge and insight, namely students can increase with information regarding what can and should not be done when using social media, information about hoaxes and cyberbullying and the ITE Law can be understood properly. The survey results also show an increase in the knowledge provided from the criteria of understanding to criteria of very understanding with an increase from the average score on the pre-test 2.96 with a percentage of 59.2% to the average score on the post-test 3.64 with a percentage of 72.8%.
Rancangan Sistem Klasifikasi Kekurangan Gizi Balita Dengan Metode K-Nearest Neighbor Syahrani Lonang; Anton Yudhana; Muhammad Kunta Biddinika
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol 5, No 1 (2023): Maret
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36499/jinrpl.v5i1.7834

Abstract

 Malnutrition in toddlers is a serious problem faced by developing countries like Indonesia, and the resulting long-term effects can reduce the intelligence of toddlers. The classification of the nutritional status of children under five is still carried out conventionally in community health centers. The K-Nearest Neighbor algorithm is included in a machine learning algorithm that can be used to classify one of the nutritional status classification problems. K-NN is used as a class determination algorithm for new data to be input according to the format. This research begins with a literature study, then identifies needs, followed by data collection that is planned to be used in the system to be built as well as a reference for making the design and the final stage of system design. This research succeeded in creating a system design using the Unified Model Language (UML), one use case that contains four functional systems, including uploading dataset files, displaying datasets, testing the accuracy of datasets, predicting new data, and designing system interfaces that will make system development easier..
Analisis Komparatif Kinerja Algoritma Machine Learning untuk Deteksi Stunting Syahrani Lonang; Anton Yudhana; Muhammad Kunta Biddinika
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 4 (2023): Oktober 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i4.6553

Abstract

Stunting is a serious problem caused by chronic malnutrition in children under five, causing stunted growth and having a negative impact on long-term health and productivity. Therefore, early detection of stunting is very important to reduce its negative impacts. Previous studies utilizing machine learning have proven the success of this method in various health applications, such as disease detection and the prediction of medical conditions. The results of this research are a comparative evaluation of five classifications, namely Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM), in classifying stunted toddlers. The dataset used contains four important attributes: age, gender, weight, and height of toddlers, as well as a binary class label that differentiates between toddlers who are stunted and those who are not. The evaluation results show that KNN at K = 3 produces the highest accuracy of 94.85%, making it the best model for classifying stunting in toddlers. Apart from accuracy, other metrics such as precision, recall, and F1-score are used to analyze the algorithm's ability to solve this problem. KNN stands out as the best model, with the highest F1-score of 89.47%. KNN also manages to maintain a balance between precision and recall, making it an excellent choice for treating stunting in toddlers. Apart from that, the use of the AUC metric from the ROC curve also shows the superiority of KNN in differentiating between stunted and non-stunting toddlers. With a combination of consistent evaluation results, both in terms of accuracy and other evaluation metrics, this research proves that KNN is the best choice for overcoming the task of classifying stunting in toddlers.
PELATIHAN DESAIN GRAFIS SEBAGAI UPAYA PENINGKATAN PENGETAHUAN DAN KETERAMPILAN DALAM PEMASARAN KONTEN SEBAGAI PELUANG MENDAPATKAN PASSIVE INCOME BAGI KARANG TARUNA CIPTA RASA DAYA DI DESA KARANG SIDEMEN Syuhada, Fahmi; Saputra, Joni; Adipta, Marazaenal; Anggarista, Randa; Kumoro, Danang Tejo; Afriansyah, M.; Lonang, Syahrani; Putra, Ahmad Fatoni Dwi; Firdaus, Asno Azzawagama; Pratama, Ramadhana Agung; Yamin, Muhamad
Jurnal Abdi Insani Vol 12 No 5 (2025): Jurnal Abdi Insani
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/abdiinsani.v12i5.2235

Abstract

The Community Partnership Empowerment activity aimed to enhance the skills and knowledge of the youth in Karang Sidemen Village, Central Lombok, in the field of digital creative economy, specifically through digital content marketing that can generate passive income. The PKM program is supported by the Directorate of Research, Technology, and Community Service through the BIMA 2024 program. The activities included a socialization session on the concept of the creative economy and technical training on using Adobe Illustrator, where participants were encouraged to market their creations on platforms like Shutterstock. The outcomes of this program showed an improvement in participants' graphic design skills, as evidenced by their ability to create logos, set up Shutterstock accounts, and independently upload their work. Additionally, this activity involved students under the Merdeka Belajar-Kampus Merdeka (MBKM) scheme, providing them with experiential learning outside the campus. In conclusion, this program successfully made a positive impact on digital literacy and the creative economy in the community and is expected to contribute to the village's economic sustainability through the empowerment of local potential in a sustainable manner.
Classification of Stunting in Toddlers using Naive Bayes Method and Decision Tree Maulana, Adrian; Ilham, Muhammad; Lonang, Syahrani; Insyroh, Nazaruddin; Sherly da Costa, Apolonia Diana; B. Talirongan, Florence Jean; Furizal, Furizal; Firdaus, Asno Azzawagama
Indonesian Journal of Modern Science and Technology Vol. 1 No. 1 (2025): January
Publisher : CV. Abhinaya Indo Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64021/ijmst.1.1.28-33.2025

Abstract

Child stunting is a health problem that has a major impact on their physical growth and brain development. This study aims to create a model that can predict the risk of stunting using machine learning technology, in order to provide assistance quickly. Using data from 7,573 children, which included information such as age, weight, height gender and breastfeeding status, we tried two methods, Naive Bayes and Decision Tree. As a result, Naive Bayes was more accurate and the success rate reached 92%, compared to Decision tree which was only 88%. With this model, it is hoped that health workers will find it easier to find children at risk of stunting, so that preventive action can be taken earlier. This research aims to provide technology-based solutions to overcome the problem of stunting in the community.
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.
Text Mining untuk Analisis Kasus Stunting di Nusa Tenggara Barat Syuhada, Fahmi; Sa'adati, Yuan; Apriani, Lia Arian; Lonang, Syahrani; Putra, Ahmad Fatoni Dwi
EDUTIC Vol 12, No 1: 2025
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/edutic.v12i1.29522

Abstract

Pemberitaan stunting yang menjadi permasalahan nasional khususnya di provinsi Nusa Tenggara Barat (NTB) sudah masif tersedia pada dunia maya beberapa tahun terakhir. Oleh sebab itu analisis terhadap trend pemberitaan kasus ini sangat menarik dilakukan. Tujuannya yaitu melihat istilah-istilah kata yang berhubungan dengan kata pada pemberitaan stunting. Dengan ini akan diketahui istilah-istilah atau kata-kata dan pola komunikasi publik terkait isu stunting di NTB. Penelitian ini  mengusulkan penerapan teknik Text Mining dalam menganalisis trend stunting NTB pada pemberitaan dunia maya. Data dikoleksi dari portal berita dengna query yang berkaitan dengan stunting NTB dari tahun 2018 hingga 2024. Penerapan metode text mining seperti preprocessing, ekslorasi data (EDA) dan Latent Dirichlet Allocation (LDA) hingga visualisasi hasil digunakan untuk reprensi trend tersebut. Kontribusi penelitian difokuskan pada bagaimana analisis trend berdasarkan tiga kumpulan corpus yaitu trend Utama Stunting, Sebab, dan Dampak. Analisis tren NTB menunjukkan bahwa istilah stunting, gizi, dan air bersih mendominasi pemberitaan, mencerminkan fokus pada faktor kesehatan utama dalam pencegahan stunting. Faktor sosial seperti nikah muda dan pendidikan juga memiliki hubungan signifikan, menunjukkan perlunya pendekatan yang mencakup dimensi sosial dan budaya. Sementara itu, intervensi berbasis komunitas, seperti posyandu, berperan penting dalam mendukung edukasi dan pemantauan gizi anak. Namun, istilah geografis seperti Bima, meskipun sering muncul, tidak memiliki hubungan langsung dengan stunting, melainkan lebih terkait dengan konteks administratif. Keseluruhan analisis menegaskan perlunya pendekatan terintegrasi yang melibatkan faktor kesehatan, sosial, dan komunikasi publik yang efektif untuk menekan prevalensi stunting di NTB.
Performance Analysis for Classification of Malnourished Toddlers Using K-Nearest Neighbor Lonang, Syahrani; Yudhana, Anton; Biddinika, Muhammad Kunta
Scientific Journal of Informatics Vol 10, No 3 (2023): August 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v10i3.45196

Abstract

Purpose: Malnutrition in toddlers is a nutritional issue that Indonesia is still dealing with. Toddlers can suffer from decreasing cognitive and physical abilities, as well as being categorized as having a high risk of death. Early detection is crucial for preventing this and providing appropriate treatment if malnutrition is detected. Classification is a machine-learning technique widely used in disease detection. Because it is simple and easy to implement, K-Nearest Neighbor is the most used classification algorithm. Detecting malnutrition can be done automatically and more quickly by utilizing classification and machine learning algorithms. The aim of this study was to analyze performance to find out which model is best for detecting malnutrition by evaluating the performance of classification using KNN with the Euclidean distance function.Methods: The dataset used in this study is the nutritional status of toddlers from Puskesmas Ubung. The classification method proposed in this research is the KNN algorithm with Euclidean distance. There are three scenarios for the classification model that will be used. Performance classification will compare each model in terms of accuracy, precision, recall, f1-score, and mean absolute error.Results: The experimental results show that KNN k = 15 using the first model generates excellent classification when classifying malnourished toddlers using the Euclidean distance function. The model obtains 91% accuracy, 86.6% precision, 83.8% recall, 85.2% recall, and a mean absolute error of 0.09.Novelty: In this experiment, we analyzed the performance of the KNN to classify malnourished children using a nutritional status dataset, which resulted in an excellent classification that could be used for early detection.
Co-Authors Adipta, Marazaenal Afriansyah, M. Anggarista, Randa Anton Yudhana Asno Azzawagama Firdaus B. Talirongan, Florence Jean Desmita, Nindri Lia Dikky Praseptian M Dwi Normawati, Dwi Faiz Isnan Abdurrachman Furizal, Furizal Herman Herman Imam Riadi Insyroh, Nazaruddin Joni Saputra Kumoro, Danang Tejo Maulana, Adrian Muhamad Yamin Muhammad Ilham Muhammad Kunta Biddinika Muhammad Kunta Biddinika Pratama, Ramadhana Agung Putra, Ahmad Fatoni Dwi Sa'adati, Yuan Sherly da Costa, Apolonia Diana Syuhada, Fahmi