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All Journal Jurnal Ilmiah KOMPUTASI Sinkron : Jurnal dan Penelitian Teknik Informatika Journal of Islamic Psychology and Behavioral Sciences JUDIKIS: Jurnal Pendidikan Islam INOVTEK Polbeng - Seri Informatika
Chairani
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Religion Humanities Computer Science & IT Education Public Health Social Sciences Other

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Prediction of Stunting in Toddlers Using Bagging and Random Forest Algorithms Juwariyem; Sriyanto; Sri Lestari; Chairani
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13448

Abstract

Stunting is a condition of failure to thrive in toddlers. This is caused by lack of nutrition over a long period of time, exposure to repeated infections, and lack of stimulation. This malnutrition condition is influenced by the mother's health during pregnancy, the health status of adolescents, as well as the economy and culture and the environment, such as sanitation and access to health services. To find out predictions of stunting, currently we still use a common method, namely Secondary Data Analysis, namely by conducting surveys and research to collect data regarding stunting. This data includes risk factors related to stunting, such as maternal nutritional status, child nutritional intake, access to health services, sanitation, and other socioeconomic factors. This secondary data analysis can provide an overview of the prevalence of stunting and the contributing factors. To overcome this, the right solution is needed, one solution that can be used is data mining techniques, where data mining can be used to carry out analysis and predictions for the future, and provide useful information for business or health needs. Based on this analysis, this research will use the Bagging method and Random Forest Algorithm to obtain the accuracy level of stunting predictions in toddlers. Bagging or Bootstrap Aggregation is an ensemble method that can improve classification by randomly combining classifications on the training dataset which can reduce variation and avoid overfitting. Random Forest is a powerful algorithm in machine learning that combines decisions from many independent decision trees to improve prediction performance and model stability. By combining the Bagging method and the Random Forest algorithm, it is hoped that it will be able to provide better stunting prediction results in toddlers. This research uses a dataset with a total of 10,001 data records, 7 attributes and 1 attribute class. Based on the test results using the Bagging method and the Random Forest algorithm in this research, the results obtained were class precision yes 91.72%, class recall yes 98.84%, class precision no 93.55%, class recall no 65.28%, and accuracy of 91.98%.
SOCIAL INTERACTIONS WITH TUNAGRAHITA CHILDREN AT SLB YPAC PALEMBANG Natasya Rifda Hanifah; Winda Agustia Anggarini; Alya Rizky Nur Kamila Wagiman; Hanna Azzahra Nabella; Yustika Pratiwi; Yudi Latama; Syelina Rizki Tria Umami; Ghaliyatul Ningtyas; Muhammad Feriyansyah; Regina Athia Mayalianti; Cherlin Vinanditha; Nindy Alfatikhatus Salamah; Raudhatul Fauziah; Artika Adi Prasetiani; Chairani
Journal of Islamic Psychology and Behavioral Sciences Vol. 1 No. 2 (2023): Journal of Islamic Psychology and Behavioral Sciences
Publisher : CV. Doki Course and Training

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61994/jipbs.v1i2.5

Abstract

This research was conducted to find out the social interactions of mentally retarded children while they were at the Palembang Special School for the Development of Disabled Children (SLB YPAC). The research method used is a qualitative research method with data collection techniques through interviews and observation. The subjects in this study were four grade C junior high school students at SLB YPAC Palembang, namely MS, M, A and K. Based on the results of the study it can be concluded that the way of social interaction for mentally retarded children is the same as the way of social contact and communication in accordance with the conditions of social interaction.
Visualisasi Data Mahasiswa Baru Tahun 2022 Di Institut Agama Islam Negeri Metro Menggunakan Google Looker Studio Ramadhan, Apri; Putra, Dittha Winyana; Chairani
Jurnal Ilmiah Komputasi Vol. 22 No. 4 (2023): Jurnal Ilmiah Komputasi : Vol. 22 No 4, Desember 2023
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32409/jikstik.22.4.3492

Abstract

Pada tahun 2022, Institut Agama Islam Negeri Metro menerima mahasiswa baru jenjang Strata 1 (S1) sebanyak 1122 orang. Data ini didapatkan berdasarkan data yang ada dalam Sistem Akademik (SISMIK) milik Institut Agama Islam Negeri Metro. Data yang disajikan dalam sistem belum tervisualisasi dengan baik sehingga informasi yang didapat tidak maksimal. Contoh data sebaran asal sekolah mahasiswa baru belum tervisualisasikan dengan baik pada sistem tersebut sehingga bagi pengguna data akan kesulitan dalam mencari informasi terkait ini. Berasal dari contoh yang disebutkan, maka visualisasi data sangat diperlukan untuk mempresentasikan data dalam format grafis atau dalam bentuk gambar agar lebih mudah dipahami. Pada penelitian ini dan berdasarkan penelitian terdahulu, maka peneliti akan menerapkan visualisasi data mahasiswa baru tahun 2022 menggunakan Google Data Studio/Google Looker Studio dengan fokus terhadap sebaran asal sekolah mahasiswa baru tahun 2022. Dashboard digital Google Data Studio/Google Looker Studio memungkinkan tampilan data dalam berbagai bentuk seperti tabel, grafik, dan peta yang membuatnya lebih menarik dan berguna bagi pengguna. Hasil dari penelitian didapat bahwa jumlah mahasiswa baru di IAIN Metro pada tahun 2022 yang mencapai 1122 memiliki sebaran asal sekolah mulai dari SMAN, MAS, MAN, SMAS, SMKN, SMKS, PONTREN, dan Paket C. Pada nama sekolah, posisi pertama berasal dari MAN 1 Lampung Timur dengan jumlah sebanyak 44 orang. SMAN 2 Sekampung dan SMAN 5 Metro berjumlah sama yaitu 17 orang. Mahasiswa yang berasal dari SMAN 3 Metro sebanyak 15 orang.
The Fundamentals of Islamic Education Reni Cahyati; Meilan Kiftya; Chairani
Jurnal Pendidikan Islam Vol. 2 No. 1 (2025): Januari
Publisher : Yayasan Tahfidzul Qur'an Al-Fawwaz

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70938/judikis.v2i1.69

Abstract

The fundamentals of Islamic education are the reference point for the source of truth and strength that can lead to the desired activities. The Quran, as the first and primary source in Islamic teachings, teaches and invites humans to always use their intellect and thinking. Islamic education is based on the elements of values contained in the teachings of Islam. One of its goals is to educate the public about the true faith to become true Muslims The purpose of this research is to assess the effectiveness of Islamic education in achieving established educational goals. This research aims to find out how and how to conduct an evaluation, so that in the teaching and learning process of a school can be seen the developments that occur, so that an educator can choose what learning methods and methods are suitable for teaching students so that the students can understand and also understand the learning provided by the teacher, so this evaluation is very important to be carried out in schoolsThe method used in this research is a literature review, gathering information from journals, the internet, and other relevant sources. The research findings show that the fundamentals of Islamic education have very important components in education and in achieving good educational goals.
Analyzing User Acceptance of NFJuara Mobile Application Using TAM and D&M IS Success Model Koim, Muhammad; Wasilah; Chairani; Sriyanto; Sri Lestari
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15209

Abstract

This study purposes to know how NFJuara application is accepted by the users in Nurul Fikri Lampung using the Technology Acceptance Model (TAM) Integrated with D&M IS Success Model. Data was collected by a validated questionnaire with inner model and outer model testing using PLS-SEM software SmartPLS. The type of data in this study is a quantitative approach. The number of samples collected was 143 respondents. Results of this research show that one of the hypotheses is rejected, that is, Service Quality (SEQ) does not affect Perceived Usefulness (PU) significantly. Besides that, this study shows that Perceived Usefulness (PU) and Perceived Ease of Use (PEU) affect as significant Acceptance of IT (AI) with R2=0.59 (Moderate) and β=0,36 (PUàAI), β=0,46 (PEUàAI). These findings imply that developers of NFJuara applications need to improve the service quality to increase acceptance, although overall NFJuara application is accepted by the user because they still feel the benefits and usefulness of the application. The contribution of this study lies in testing the technology acceptance model in the context of mobile learning, which enriches the literature on the adoption of application-based e-learning, as well as providing practical recommendations for application developers to enhance user experience.  
Phishing Website Detection Using a Machine Learning Classification Approach Ibnu Arifin; Chairani
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/yja1d830

Abstract

Phishing is a form of cybercrime that is increasingly prevalent, with millions of attacks recorded annually. This study develops a phishing website detection model using a machine learning classification approach, employing a pipeline that includes data preprocessing, feature selection, and model validation. The dataset was obtained from the UCI Machine Learning Repository and consists of 235,795 URLs with a relatively balanced distribution between phishing (100,945) and non-phishing (134,850). After data cleaning and feature selection, 21 optimal features were retained, ensuring they were safe from potential data leakage. Two algorithms were evaluated: decision tree and random forest, using 10-fold cross-validation. The random forest algorithm achieved an average accuracy of 97.78%, while the decision tree was slightly higher at 98.02%. However, random forest outperformed in class discrimination, as measured by ROC-AUC (99.73%) and PR-AUC (99.78%), compared to decision tree values of 99.49% and 99.40%. The method also incorporated a 10-fold cross-validation procedure to minimize data leakage and ensure reliable model evaluation. The Wilcoxon test further confirmed that the performance difference between the two algorithms is statistically significant. Overall, although the decision tree demonstrates strong classification performance, random forest proves to be more consistent and reliable in detecting phishing websites, making it a superior choice in the context of cybersecurity.
Co-Authors Alya Rizky Nur Kamila Wagiman Apri Ramadhan, Apri Artika Adi Prasetiani Cherlin Vinanditha Dittha Winyana Putra Ghaliyatul Ningtyas Hanna Azzahra Nabella ibnu arifin Juwariyem Koim, Muhammad Meilan Kiftya Muhammad Feriyansyah Natasya Rifda Hanifah Nindy Alfatikhatus Salamah Raudhatul Fauziah Regina Athia Mayalianti Reni Cahyati SRI LESTARI Sriyanto Syelina Rizki Tria Umami Wasilah Winda Agustia Anggarini Yudi Latama Yustika Pratiwi