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All Journal Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Smatika Jurnal : STIKI Informatika Jurnal
Erni
Universitas Bina Sarana Informatika
Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering

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Analisis Kinerja Random Forest, Gradient Boosting, dan LightGBM dengan SMOTENC pada Klasifikasi Tingkat Obesitas Kartika Handayani; Erni; Fuad Nur Hasan
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 5 No. 1 (2026): Juni 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v5i1.1074

Abstract

Klasifikasi tingkat obesitas merupakan aspek penting dalam bidang kesehatan untuk mendukung deteksi dini dan pengambilan keputusan yang tepat. Namun, dataset obesitas umumnya memiliki distribusi kelas yang tidak seimbang, yang dapat menyebabkan bias model terhadap kelas mayoritas dan menurunkan kemampuan dalam mengenali kelas minoritas. Penelitian ini bertujuan untuk menganalisis kinerja algoritma ensemble learning dalam klasifikasi tingkat obesitas serta mengevaluasi pengaruh teknik penyeimbangan data terhadap performa model. Algoritma yang digunakan meliputi Random Forest, Gradient Boosting, dan Light Gradient Boosting Machine (LightGBM). Untuk mengatasi ketidakseimbangan data, diterapkan metode Synthetic Minority Over-sampling Technique for Nominal and Continuous (SMOTENC). Evaluasi model dilakukan menggunakan metrik accuracy, precision, recall, dan F1-score, serta didukung oleh analisis confusion matrix dan kurva Receiver Operating Characteristic (ROC). Hasil penelitian menunjukkan bahwa model LightGBM dengan SMOTENC menghasilkan performa terbaik dengan nilai accuracy sebesar 96,45%, precision sebesar 96,12%, recall sebesar 95,98%, dan F1-score sebesar 96,05%. Penerapan SMOTENC terbukti meningkatkan nilai recall dan F1-score pada kelas minoritas, sehingga menghasilkan klasifikasi yang lebih seimbang dan mengurangi bias terhadap kelas mayoritas.
Pengembangan Sistem Informasi Pencatatan Hasil Panen Kelapa Sawit Berbasis Web Abdullah Muhaimin; Deni Risdiansyah; Erni
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 5 No. 1 (2026): Juni 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v5i1.1197

Abstract

Information Systems, Oil Palm, Harvest Yield Recording, Web-Based System, Waterfall, SvelteKit, Oil Palm Farmers
Pengembangan Aplikasi Presensi Karyawan Berbasis Mobile dengan Face Recognition dan Geolocation (Studi Kasus: PT. Visionet Data Internasional Service Point Pontianak) Boy Pratama; Deni Risdansyah; Erni
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 5 No. 1 (2026): Juni 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v5i1.1258

Abstract

Transformasi digital dalam pengelolaan sumber daya manusia mendorong perusahaan untuk menerapkan sistem presensi yang lebih efektif dan akurat. PT Visionet Data Internasional Service Point Pontianak telah menggunakan sistem presensi berbasis web, namun penggunaannya masih menghadapi kendala berupa keterbatasan akses, kurang optimalnya pemantauan kehadiran, serta belum adanya mekanisme validasi identitas dan lokasi secara langsung. Penelitian ini bertujuan merancang dan membangun aplikasi presensi karyawan berbasis mobile dengan memanfaatkan teknologi Face Recognition dan Geolocation sebagai metode validasi kehadiran. Metode pengembangan sistem yang digunakan adalah Waterfall yang terdiri atas tahap analisis kebutuhan, perancangan sistem, implementasi, pengujian, dan pemeliharaan. Aplikasi dikembangkan menggunakan framework Flutter dan bahasa pemrograman Dart, sedangkan sistem backend dibangun menggunakan Laravel dengan basis data MySQL. Fitur yang diimplementasikan meliputi registrasi wajah, validasi lokasi, presensi masuk, presensi pulang, dan pengajuan izin. Hasil penelitian menunjukkan bahwa aplikasi mampu melakukan verifikasi identitas pengguna melalui pengenalan wajah serta memastikan lokasi presensi sesuai dengan area kerja yang telah ditentukan. Sistem yang dibangun dapat meningkatkan akurasi pencatatan kehadiran, mengurangi potensi manipulasi data presensi, dan mempermudah pengelolaan data kehadiran secara terintegrasi.
Rancang Bangun Sistem Informasi Rental Mobil Berbasis Web Terintegrasi WhatsApp Chatbot CV Domia Group Flavius Pratama Putra; Deni Risdiansyah; Erni
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 5 No. 1 (2026): Juni 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v5i1.1271

Abstract

Abstract The development of information technology has brought fundamental changes to various industrial sectors, including the vehicle rental business. CV Domia Group still faces operational problems in managing vehicle data, consumer data, rental transactions, and information services that are still carried out conventionally. This study aims to design and implement a Web-Based Car Rental Information System integrated with WhatsApp Chatbot as a means of automatic vehicle booking. The system development method applied is the Waterfall model with the stages of needs analysis, design, implementation, testing, and maintenance. The system was built using the PHP programming language with the CodeIgniter 3 framework and MySQL database, and integrated with WhatsApp Chatbot through the SunWa Application Programming Interface to automate information services and vehicle bookings. Test results using the Black Box Testing method show that all system functionality runs according to user needs. The developed system is proven to optimize the integrated management of car rental operational data, shorten the vehicle booking process, and improve the effectiveness of service to CV Domia Group Sanggau customers.
Sistem Informasi Layanan Desa Berbasis Web Terintegrasi WhatsApp Chatbot untuk Pengajuan Surat Jonius Gunadi; Deni Risdiansyah; Erni
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 5 No. 1 (2026): Juni 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v5i1.1284

Abstract

Pelayanan administrasi desa yang masih dilakukan secara manual menyebabkan proses pengajuan surat, penyampaian informasi, dan pengaduan masyarakat menjadi kurang efektif. Masyarakat juga sering mengalami kesulitan memperoleh informasi mengenai persyaratan administrasi serta harus datang langsung ke kantor desa untuk mengakses layanan. Penelitian ini bertujuan merancang, membangun, mengimplementasikan, dan menguji sistem informasi pelayanan desa berbasis web yang terintegrasi dengan WhatsApp Chatbot untuk mendukung pengajuan surat dan pengaduan masyarakat. Metode pengembangan sistem yang digunakan adalah Waterfall yang meliputi analisis kebutuhan, perancangan, implementasi, pengujian, dan pemeliharaan. Sistem dikembangkan menggunakan website sebagai media pengelolaan data publikasi, pengajuan surat, dan pengaduan masyarakat, sedangkan WhatsApp Chatbot digunakan untuk memberikan informasi persyaratan administrasi, menerima pengajuan surat, dan menampung pengaduan secara otomatis. Pengujian sistem dilakukan menggunakan metode Black Box Testing. Hasil penelitian menunjukkan bahwa seluruh fungsi sistem berjalan sesuai dengan kebutuhan pengguna. Sistem mampu mengelola publikasi desa, pengajuan surat, dan pengaduan masyarakat melalui website serta menyediakan layanan otomatis melalui WhatsApp Chatbot. Dengan demikian, sistem yang dikembangkan dapat meningkatkan efektivitas pelayanan administrasi desa, mempercepat penyampaian informasi, serta memudahkan masyarakat mengakses layanan kapan saja melalui WhatsApp.
Implementation of Ridge Regression and SHAP for Analyzing Anxiety Levels Based on the Digital Behavior of Social Media Users Yuri Yuliani; Kukuh Panggalih; Kudiantoro Widianto; Erni; M. Iqbal Alifudin; Irwan Herliawan
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 16 No 02 (2026): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v16i02.2370

Abstract

Advances in technology have led to an increase in the frequency of smartphone, social media, and various digital app usage in daily life. These digital activities give rise to various digital behaviors—such as screen time, number of notifications, social media usage, and sleep patterns—which can be evaluated to understand users mental health. High digital device usage is suspected to be associated with increased anxiety levels among users. Therefore, this study aims to analyze the impact of digital behaviors on anxiety levels by utilizing explainable machine learning approaches and AI. This study uses the dataset, comprising 500 data points obtained from Kaggle. The research stages consist EDA, feature engineering, target leakage evaluation, comparison of several machine learning algorithms, cross-validation, and model interpretation using SHAP. Based on the EDA results, the variables social_media_time_min, notification_count, and digital_addiction_score showed a positive relationship with anxiety_level. During the model-building process, the variable digital_wellbeing_score was removed because it had a very high correlation with anxiety_level -0.84, which could lead to potential target leakage. The algorithm comparison revealed that the Ridge Regression model performed best compared to Random Forest, SVR, and XGBoost Regression, with an R² score of 0.221 and an RMSE of 1.627. Additionally, the results of 5-Fold Cross Validation showed an average R² Score of 0.158 with a standard deviation of 0.062, indicating that the model demonstrated fairly consistent performance. The SHAP interpretation reveals that notification_count and social_media_time_min are the variables that most strongly influence the prediction of anxiety_level. The results of this study indicate that digital behavior affects users anxiety levels, although the relationships among the variables remain quite complex. This study also emphasizes the importance of evaluating the target leakage and understanding the model in the development of machine learning-based mental health analysis to ensure that the prediction results are more objective and clear.
Comparison of Random Forest and K-Nearest Neighbors in Heart Disease Prediction Erni; Ibnu Alfarobi; Wawan Kurniawan
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1942

Abstract

Heart disease is one of the leading causes of death worldwide, with a death toll reaching 17.9 million cases annually according to the World Health Organization (WHO) and a prevalence of 1.5% in Indonesia. This high mortality rate demonstrates the importance of early detection and accurate prediction to prevent more serious complications. The development of artificial intelligence technology, particularly machine learning, offers a new approach in the medical field through the ability to analyze clinical data quickly and efficiently. This study was conducted to compare the performance of two machine learning algorithms, namely Random Forest and K-Nearest Neighbors (KNN), in predicting heart disease using a clinical dataset from Kaggle containing 20 samples and 9 attributes related to the patient's physiological condition. The parameter optimization process in both algorithms was carried out using grid search techniques with cross-validation to obtain the best model that can perform optimally on a limited dataset. Performance evaluation was carried out using accuracy, recall, and precision metrics to comprehensively measure the quality of the model predictions. The results of the study showed that the Random Forest algorithm provided superior performance with an accuracy of 0.75, a recall of 0.88, and a precision of 0.86, compared to KNN which only achieved an accuracy of 0.50, a recall of 0.67, and a precision of 0.67. These findings indicate that Random Forest is more effective in identifying the presence of heart disease, especially in terms of sensitivity to positive cases and prediction consistency. Thus, Random Forest has the potential to be a more appropriate algorithm for implementation in machine learning-based clinical decision support systems, to support the process of diagnosing heart disease more accurately and efficiently.
Co-Authors Abdullah Muhaimin Boy Pratama Deni Risdansyah Flavius Pratama Putra Fuad Nur Hasan Ibnu Alfarobi Irwan Herliawan Jonius Gunadi Kartika Handayani Kudiantoro Widianto, Kudiantoro Kukuh Panggalih M. Iqbal Alifudin Risdiansyah, Deni Wawan Kurniawan Yuliani, Yuri