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Kusrini, Kusrini

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ANALISIS PERBANDINGAN ALGORITMA KLASIFIKASI UNTUK IDENTIFIKASI DIABETES DENGAN MENGGUNAKAN METODE RANDOM FOREST DAN NAIVE BAYES Zuhri, Muhammad Rafli; Kusrini, Kusrini; Ariatmanto, Dhani
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 7 No 1 (2025): EDISI 23
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v7i1.5146

Penanganan penyakit diabetes menjadi penting karena komplikasi yang dapat terjadi jika tidak ditanggulangi dengan benar. Klasifikasi merupakan salah satu metode yang dapat digunakan untuk mengidentifikasi diabetes. Algoritma klasifikasi ini dapat menganalisis data pasien, seperti usia, jenis kelamin, riwayat kesehatan, dan hasil tes, untuk memprediksi apakah pasien tersebut menderita diabetes. Random Forest dan Naïve Bayes merupakan dua algoritma klasifikasi yang populer. Random Forest adalah metode kompleks yang didasarkan pada penggabungan beberapa pohon keputusan untuk mendapatkan prediksi yang lebih akurat, sedangkan Naïve Bayes merupakan metode pengklasifikasian berdasarkan probabilitas sederhana dan dirancang agar dapat dimanfaatkan denegan asumsi antar variabel penjelas saling bebas (independen). Hasil penelitian menggunakan data sebanyak 70% sebagai data pelatihan dan 30% sebagai data pengujian dari keseluruhan 768 data. keseluruhan yang diperoleh bahwa metode random forest dapat memprediksi penyakit diabetes dengan tingkat persentase sebesar 94% dan tingkat persentase naïve bayes sebesar 78%. Berdasarkan hasil penelitian yang diperoleh metode random forest memiliki tingkat persentase lebih tinggi dibandingkan metode naïve bayes dengan tingkat persentase 94% sedangkan naïve bayes dengan tingkat persentase 78% sehingga dapat disimpulkan bahwa metode random forest merupakan metode terbaik dalam mengindentifikasi penyakit diabetes dibandingkan metode naïve bayes.

Optimasi Prediksi Harga Emas Menggunakan CNN-Bi-LSTM dengan Mekanisme Attention dan Bayesian Optimization Fitriyanto, Nur; Kusrini, Kusrini
Journal of Economic, Management, Accounting and Technology (JEMATech) Vol 8 No 1 (2025): Februari
Publisher : Fakultas Teknik dan Ilmu Komputer, Universitas Sains Al-Qur'an (UNSIQ) Wonosobo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32500/jematech.v8i1.8668

Prediksi harga emas merupakan aspek penting dalam investasi global karena volatilitasnya yang dipengaruhi oleh faktor ekonomi dan politik. Penelitian ini mengembangkan model hybrid CNN-Bi-LSTM dengan mekanisme Attention untuk menangkap pola data signifikan dan Bayesian Optimization untuk pencarian hyperparameter yang lebih efisien. Dataset yang digunakan mencakup harga emas harian dari 29 Desember 1978 hingga 4 Juni 2021, yang terbagi menjadi data pelatihan (70%), validasi (20%), dan pengujian (10%). Model yang dioptimasi menunjukkan hasil evaluasi dengan RMSE sebesar 17,98, MAE sebesar 10,93, RMAE sebesar 3,31, dan R² sebesar 1,00. Visualisasi hasil menunjukkan konvergensi stabil tanpa overfitting, distribusi residual yang mendekati normal, serta prediksi yang konsisten dengan data aktual. Integrasi mekanisme Attention dan Bayesian Optimization terbukti meningkatkan performa model secara signifikan. Penelitian ini membuka peluang pengembangan lebih lanjut dengan memasukkan variabel makroekonomi tambahan, seperti harga minyak mentah atau indeks saham, untuk memperluas cakupan prediksi.

Enhancing fire detection capabilities: Leveraging you only look once for swift and accurate prediction Nugroho, Agung; Agastya, I Made Artha; Kusrini, Kusrini
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1326-1334

Detecting fires is crucial to prevent potentially catastrophic outcomes. Traditional fire detection methods, relying on electronic, chemical, or mechanical sensors, often suffer from time delays in activation due to threshold parameters. An emerging alternative utilizes artificial intelligence, particularly image-based fire detection, using convolutional neural networks (CNNs). You only look once (YOLO) is a state-of-the-art object detection framework prized for speed and real-time capabilities. In our research, we conducted multiple training experiments employing various deep neural network (DNN) architectures as feature extractors for object detection within the YOLOv5 framework. These architectures included MobileNetV3, ResNet, and CSP-Darknet53. Among these configurations, YOLOv5 with CSP-Darknet53 (scale s) emerged as the most accurate, boasting mAP@50 of 0.88 and an impressive FPS of 73, with training model size of 14.50 MB. Furthermore, we integrated the selected model with the streamlit package to create a user-friendly web application interface for fire detection testing. The resulting model demonstrates remarkable efficiency, detecting fires within 0.01 seconds. This research represents a significant advancement in fire detection technology, offering both rapid detection and enhanced accuracy, with potential applications in various settings, from indoor facilities to outdoor environments.

Environmental Acoustic Features Robustness Analysis: A Multi-Aspecs Study Semma, Andi Bahtiar; Kusrini, Kusrini; Setyanto, Arif; da Silva, Bruno; Braeken, An
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 9 No 1 (2025): February 2025
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v9i1.23723

Abstract—Background: Acoustic signals are complex, with temporal, spectral, and amplitude variations. Their non-stationarity complicates analysis, as traditional methods often fail to capture their richness. Environmental factors like reflections, refractions, and noise further distort signals. While advanced techniques such as adaptive filtering and deep learning exist, comprehensive acoustic feature analysis remains limited. Objective: This study investigates which acoustic features maintain the highest robustness across diverse environments while preserving discriminative power. Methods: Audio samples were recorded in controlled environments (jungles, cafés, factories, streets) with varying noise levels. Standardized equipment captured 22050 Hz, 16-bit audio at multiple positions and distances. After amplitude standardization, various acoustic features were extracted and analyzed. Results: MFCCs demonstrated exceptional reliability, with correlation coefficients of 0.98819 and 0.98889 for closely positioned devices and a robustness score of 0.99. Across different acoustic scenes and sample lengths (1, 3, 5s), MFCCs maintained high correlation (≈0.978) and robustness (0.98), confirming their versatility. Conclusion: MFCCs proved highly effective for acoustic fingerprinting across settings. Despite limitations in tested environments (≤5m distance, ≤5s samples), their consistent performance validates the methodology. Future research should explore combining MFCCs with spectral features and expanding studies to broader environments and device types.

SISTEM KLASIFIKASI PENYAKIT JANTUNG BERBASIS PARTICLE SWARM OPTIMIZATION DAN NAÏVE BAYES DENGAN 5-FOLD CROSS VALIDATION Mufti Ari Bianto; Kusrini, Kusrini
Journal of Innovation Research and Knowledge Vol. 3 No. 8: Januari 2024
Publisher : Bajang Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar

Serangan Jantung adalah salah satu penyakit yang paling mematikan tercatat di dunia, terdapat jumlah kasus baru Penyakit Jantung sebanyak 1,5% serta jumlah kematian sebanyak 14,38%. Pada tahun 2022 jumlah penderita Penyakit Jantung di Indonesaia sejumlah 139.891 orang, pada umumnya jumlah penderita penyakit ini terus meningkat dikarenakan kurangnya pengetahuan atau informasi tentang penyakit jantung tersebut. Oleh karena itu dibutuhkan sebuah sistem yang dapat memberikan informasi serta klasifikasi penyakit secara dini yang dapat digunakan untuk klasifikasi apabila seseorang ingin mengetahui informasi ataupun gejala awal serangan jantung. Metode naïve bayes merupakan salah satu metode yang digunakan untuk melakukan klasifikasi berdasarkan probabilitas atau kemungkinan dari data sebelumnya, selain pendekatannya sederhana metode tersebut juga dapat melakukan klasifikasi secara baik. Mekanisme pengujiannya yaitu membagi 303 data kedalam 5 subset yang akan divalidasi dengan 5-fold cross validation. Hasil akhir dari penelitian ini adalah penerapan sistem klasifikasi dengan menggunakan metode naïve bayes yang akan menghasilkan nilai rata-rata akurasi sebesar 90,61%, presisi sebesar 87,44 %, dan recall sebesar 87,95%.

Prediksi Transaksi Minat Pembelian Online Menggunakan Kombinasi CNN Conv1D dan BiLSTM Herawati, Maimi; Kusrini, Kusrini
Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics Vol 11 No 1 (2025): Journal CERITA : Creative Education of Research in Information Technology and Ar
Publisher : UNIVERSITAS RAHARJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/cerita.v11i1.3702

The rapid development of information technology has transformed consumer shopping behavior, particularly through e-commerce platforms. Online shopping has become a primary trend due to its convenience and the growing penetration of the internet. Understanding online purchase intention is therefore crucial for businesses in devising effective marketing strategies. Purchase intention is influenced by factors such as product quality, price, customer reviews, and platform usability. However, predicting purchase intention poses a significant challenge due to the large and complex nature of consumer data. Smote used for imbalance data. This study aims to combine CNN (Conv1D) and BiLSTM for high-accuracy purchase intention prediction. The research focuses on analyzing model accuracy and the effectiveness of the algorithms in handling imbalanced data. The results indicate that the combined CNN(Conv1D) + BiLSTM model achieves 97% accuracy with balanced evaluation metrics, although the True class recall (96%) is slightly lower than that of the False class (95%). Further optimization is needed to enhance overall model performance.

Rainfall Prediction in Jayapura City Area Using Long Short-Term Memory Azi, Amanda; Kusrini, Kusrini
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 2 (2025): Forthcoming: Research Article, Volume 7 Issue 2 April, 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i2.5506

Jayapura, one of Indonesia’s major fishing cities, relies heavily on accurate weather predictions to ensure the safety of its fishermen, particularly due to its significant tuna and skipjack production. This study aims to improve rainfall forecasting in Jayapura using a Long Short-Term Memory (LSTM) model, a type of artificial neural network designed for time series prediction. Accurate rainfall forecasts are crucial for reducing the risks fishermen face at sea due to sudden weather changes. Daily data from the Meteorological Station in Dok II Jayapura was collected and processed to train the LSTM model, incorporating variables such as TAVG (average temperature), RH_AVG (average relative humidity), FF_AVG (average wind speed), Pressure (air pressure), and Wind_Gust (wind gust). The model’s performance was evaluated using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), yielding low values of 0.0542 and 0.0847, respectively, indicating high prediction accuracy. The MAE reflects the average magnitude of errors, while the RMSE highlights the model’s sensitivity to larger deviations, both supporting the reliability of the LSTM approach. The findings demonstrate that LSTM models can effectively forecast rainfall in Jayapura, providing valuable information that helps fishermen plan their activities more safely and efficiently. The study concludes that LSTM is a robust tool for rainfall prediction, and the inclusion of additional meteorological variables has proven to enhance accuracy. Further research is recommended to explore other factors to improve prediction reliability.

EFEKTIVITAS PROGRAM JELITA JIWA SEBAGAI INOVASI PELAYANAN DI DINAS KEPENDUDUKAN DAN PENCATATAN SIPIL KABUPATEN SLEMAN Hasan, Nurul Rahmawati; Nugroho, Hanantyo Sri; Kusrini, Kusrini; Muhammad, Alva Hendi
PRAJA: Jurnal Ilmiah Pemerintahan Vol 12 No 2 (2024): Edisi Juni
Publisher : FISIP Universitas Muhammadiyah Sidenreng Rappang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55678/prj.v12i2.1548

Pelayanan administrasi kependudukan merupakan salah satu pelayanan publik yang bertanggungjawab dalam memberikan pelayanan administrasi kependudukan kepada masyarakat untuk digunakan dalam keperluan pemenuhan hak dasar warga negara. Melalui Peraturan Dalam Negeri Nomor 19 Tahun 2018 tentang Peningkatkan Kualitas Layanan Administrasi Kependudukan mengembangkan inovasi pelayanan dalam rangka peningkatan kualitas administrasi kependudukan dapat dilakukan melalui layanan terintegrasi dan/atau jemput bola. Program Jelita Jiwa merupakan sebuah inovasi pelayanan jemput bola yang dilaksanakan oleh petugas Dinas Kependudukan dan Pencatatan Sipil Kabupaten Sleman ke rumah penduduk yang mengalami sakit berat, lansia, disabilitas, dan orang dengan gangguan jiwa (ODGJ) yang tidak memiliki KTP elektronik. Penelitian ini bertujuan untuk mengetahui efektivitas dan kendala pelaksananaan Program Jelita Jiwa dengan menggunakan indikator input, proses, dan output. Penelitian menggunakan pendekatan deskriptif kualitatif dengan teknik pengumpulan data melalui observasi, wawancara, dan dokumentasi serta analisis reduksi data, penyajian data, dan penarikan kesimpulan. Hasil penelitian menunjukan bahwa Program Jelita Jiwa di Dinas Kependudukan dan Pencatatan Sipil Sleman cukup efektif. Meskipun masih ada beberapa kendala seperti infrastruktur jalan kurang baik, lokasi tempat tinggal yang jauh, pengaturan kerja aparatur belum terorganisir dengan optimal, dan faktor kondisi pemohon itu sendiri.

Menggunakan Metode Machine Learning Untuk Memprediksi Nilai Mahasiswa Dengan Model Prediksi Multiclass Setiawan, Moh. Arif Ma'ruf; Kusrini, Kusrini; Hartono, Anggit Dwi
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 1 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i1.8334

This study aims to predict students' final GPA and study duration using machine learning methods. The model applied in this study is the Random Forest Regressor, which was trained using a dataset that includes various factors such as semester GPA, socio-economic background, demographics, learning activities, and the difficulty level of courses. The results of the study show that the model produces less accurate predictions, with a Mean Squared Error (MSE) of 0.34 for the final GPA and 3.83 for the study duration. Furthermore, the R² Score for the predictions of final GPA and study duration are -0.079 and -0.055, respectively, indicating that the model's prediction performance is not optimal. In the multiclass classification section, the model is able to classify students into several categories based on their final GPA, such as Cum Laude, Very Satisfactory, Satisfactory, and Fair. From the testing results, the model predicts a final GPA of 2.92 for a new student example, which is classified into the "Satisfactory" category, with a predicted study duration of 8 semesters. The conclusion of this study indicates that the regression model used requires improvement to achieve better accuracy. Other factors, such as feature optimization or the use of alternative algorithms, can be explored in future research to enhance the prediction results.

Rainfall Prediction in Jayapura City Area Using Long Short-Term Memory Azi, Amanda; Kusrini, Kusrini
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 2 (2025): Research Article, Volume 7 Issue 2 April, 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i2.5506

Co-Authors AA Sudharmawan, AA Abdillah, Yahya Auliya Abdullah Sukri, M Iqbal Abdullah, Mochamad Fadillah Achmad Oddy Widyantoro Ade Pujianto, Ade Adhani, Muhammad Azmi Agastya, I Made Artha agung budi AGUS PURWANTO Ahmad Yusuf Aji Santoso, Bayu Aji Susanto Anom Purnomo Alfatta, Hanif Alva Hendi Muhammad Andi Muhammad Irfan Andi Sunyoto Andika, Roy Andriyanto, Rifki Angga Kurniawan Anggit Dwi Hartanto, Anggit Dwi Anggraeni, Meita Dwi Ardana, Wildan Muhammad Ardana, Wildan Muhammmad Ardiansyah, Fachri Ari Yuana, Kumara Arief Setyanto Arief, M Rudyanto Arief, Muhammad Rudyanto Arifuddin, Danang Arik Sofan Tohir Aris Subadi Arli Aditya Parikesit Asnawi, Muhamad Fuat Atin Hasanah Azi, Amanda Aziz Muzani, Ma'ruf Aziz, Moh Abdul Azkar, Azkar Bayu Setiaji Béjar, Rodrigo Martínez Bentar Candra P Bernadhed, Bernadhed Bisono, Hadi Hikmadyo Braeken, An Buana, Yopy Tri Candra, Kurnia Khoirul da Silva, Bruno Darmawan, Eko Rahmad David Agustriawan DHANI ARIATMANTO Dzulhijjah, Dwi Ahmad Eko Pramono Eko Purwanto Ema Utami Emha Taufiq Luthfi Fatkhurrochman, Fatkhurrochman Fauzi, Moch Farid Fauzy, Marwan Noor Febrianti, Winda Febriyanti, Nada Rizki Ferry Wahyu Wibowo fitriyanto, nur Gifari, Okta Ihza Halimi, Ahmad Hamdikatama, Bimantyoso Hanafi Hanafi Hanif Al Fatta Hari Muktafin, Elik Haris, Ruby hartanto, david budi Hartono, Anggit Dwi Haryo, Wasis Hasan, Nur Fitrianingsih Hasan, Nurul Rahmawati Helmawati, Nita Herawati, Maimi Heri Abijono, Heri Herlinawati, Noor Hulvi, Alfajri I Putu Agus Ari Mahendra Ikhwanudin, Aolia Ilmawati, Fahma Inti Jeki Kuswanto Juwariyah, Siti Kasman, Haris Saktiawan Kurniasari, Iin Kusnawi , Kusnawi Kusnawi Kusnawi Lewu, Retzi Y. Linda, Kumara Dewi Listyanto, Ahmad Wildan López, Alba Puelles Lukman Bachtiar M. RUDYANTO ARIEF M. Suyanto, M. Madhika, Yudha Randa Mahendra, Awanda Putra Mangun, Syamsul Syahab Maradona, Maradona Mardiana Mardiana Martínez-Béjar, Rodrigo Masruri, Nizar Haris Masud, Ibnu maulana, fahrizal Megantara, Muhamad Arldi MEI PARWANTO KURNIAWAN Metha, Halifa Sekar Miftachuddin, Achmad Agus Athok Mohamad Firdaus, Mohamad Mohammad Diqi Mohammad Rezza Pahlevi Moningka, Nirwan Mufti Ari Bianto Muhamad Iksan, Muhamad Muhammad Resa Arif Yudianto Muktafin, Elik Hari Mulia Sulistiyono Muzakir, Muhammad MZ, Reza Rafiq Nasiri, Asro Ngaeni, Nurus Sarifatul Ni Nyoman Utami Januhari, Ni Nyoman Nugroho, Agung Nugroho, Hanantyo Sri Nuk Ghurroh Setyoningrum Nurmalasari, Maulidya Dwi Oktafiqurahman, Andi Olajuwon, Sayyid Muh. Raziq Onde, Mitrakasih La ode Oscar Samaratungga Pamoengkas, Muhamad Agoeng Pamungkas, Sapto Pradipta, Dody Prameswari, Sonia Anjani Prasetio, Agung Budi Prastyo, Rahmat Pratama, Muhammad Egy Puri, Fiyas Mahananing Purnamasari, Resti Putra, Andriyan Dwi Rachmawati Oktaria Mardiyanto RAMADHAN, SYAIFUL Rasyid, Magfirah Raynald Alfian Yudisetyanto Riduan, Nor Rizkayati, Anisa S, Muhamad Rois S, Muhammad Sabri Saleh, Robby Febrianur Samponu, Yohakim Benedictus Santosa, Hendriansyah SANTRI SANTRI Saputro, Moh. Rizal Bayu Sarawan, Tommy Sari, Yayak Kartika Selvy Megira, Selvy Semma, Andi Bahtiar Sentoso, Thedjo Setiawan, Moh. Arif Ma'ruf Setyanto, Arif Siswo Utomo, Mardi Slamet . Solikin, Arif Fajar Sudarmawan, Sudarmawan Sudarto Sudarto Swastikawati, Claudia Syafutra, Arif Dwi Syaiful Huda Tala, WD. Syarni Tampubolon, Jandri Tamuntuan, Virginia Toifur, Tubagus TONNY HIDAYAT Tri Nugroho, Arief triadin, Yusrinnatul Jinana Tukan, Ewaldus Ambrosius Ula, M. Izul Wahyu Pujiharto, Eka Wahyudi, Alfian Cahyo Wangsa, Sabda Sastra Wijaya, Jodi Wiwi Widayani, Wiwi Yanuargi, Bayu Yossy Ariyanto Yuana, Kumara Ari Yuza, Adela Zakaria Zakaria Zuhri, Muhammad Rafli Zulkarnain, Imam Alfath Zumarni, Zumarni

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