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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Komputer Terapan INOVTEK Polbeng - Seri Informatika Jurnal Informatika Universitas Pamulang JURIKOM (Jurnal Riset Komputer) Jurnal Sistem Komputer dan Informatika (JSON) Madani : Indonesian Journal of Civil Society Journal of Applied Computer Science and Technology (JACOST) Journal of Computer and Information System (J-CIS) Jurnal Pengabdian kepada Masyarakat Indonesia (JPKMI)
Nur, Nahya
Universitas Sulawesi Barat
Religion Arts Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Nursing Physics Public Health

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Optimasi Algoritma K-Nearest Neighbors pada Prediksi Penyakit Diabetes Arfiah, Sitti; Wajidi, Farid; Nur, Nahya
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8615

Abstract

Diabetes mellitus is a chronic disease characterized by high blood sugar levels due to metabolic system disturbances, specifically related to insulin production or effectiveness. If left untreated, it can lead to serious complications. Early and accurate detection is crucial for timely medical intervention. This research aimed to improve the accuracy of a diabetes classification system using the K-Nearest Neighbors (KNN) algorithm. An initial KNN model with imbalanced data (without SMOTE) and no GridSearchCV achieved only 83% accuracy. While seemingly good, its performance for the positive class was low (precision 80%, recall 69%, F1-score 74%), indicating bias towards the negative class due to data imbalance. To address this, several steps were implemented: data preprocessing (handling missing data and feature normalization), hyperparameter optimization using GridSearchCV, and data balancing with SMOTE. After these improvements, the KNN model showed significant performance gains, with accuracy reaching 94%. Performance for the positive class greatly improved (precision 90%, recall 98%, F1-score 94%), and for the negative class (precision 98%, recall 89%, F1-score 93%). These results demonstrate that combining preprocessing, model optimization, and class balancing effectively enhances the KNN algorithm's ability to detect diabetes more accurately and robustly, proving that machine learning with proper data processing can aid in developing medical decision support systems for early diabetes diagnosis.
Application of Decision Support System for Selection of Residential Criteria using the fuzzy Method in Majene Regency Irianti, Arnita; Quraisy, Muh. Imam; Sulfayanti, Sulfayanti; Nur, Nahya; Ardi, Rahmawati
Jurnal Komputer Terapan Vol 10 No 1 (2024): Jurnal Komputer Terapan
Publisher : Politeknik Caltex Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35143/jkt.v10i1.5870

Abstract

In this digital era, information technology is growing rapidly so that it is used to market various things, including homes. There is a lot of information technology about houses being marketed, but it takes a long time to collect information and compare one house to another. However, some of the housing marketed no longer prioritizes comfort, but rather cheap or economical prices. Due to the fact that sometimes unclear data is needed to solve problems, the fuzzy method is a decision-making approach that uses standard relationships but applies fuzzy set theory to the database. Decision making regarding the selection of comfortable housing criteria according to consumers is carried out using the fuzzy Tahani model approach. The fuzzy holdi technique uses house data that has been processed to produce output in the form of house data that is recommended for customers.
Prediksi Harga Gabah Kering Panen menggunakan Metode Autoregressive Integrated Moving Average Ria, Canda; Wajidi, Farid; Nur, Nahya
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 6 No. 4 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v6i4.8614

Abstract

Gabah kering panen (GKP) merupakan komoditas pertanian strategis yang berperan signifikan dalam mendukung ketahanan pangan nasional. Fluktuasi harga gabah yang tidak menentu menjadi tantangan serius bagi petani dalam menentukan harga jual hasil panen. Dalam penelitian ini, harga gabah kering panen diprediksi dengan menggunakan metode   Autoregressive Integrated Moving Average (ARIMA). Data yang digunakan diambil dari data bulanan yang dikumpulkan oleh Badan Pusat Statistik (BPS) dari januari 2010 hingga desember 2024. Proses penelitian meliputi tahap pengumpulan data, pengolahan data, uji stasioneritas menggunakan Augmented Dickey-Fuller (ADF), analisis Autocorrelation Function (ACF) dan Partial Autocorrelation Function (PACF), pemilihan parameter optimal (p, d, q) menggunakan pendekatan grid search, pembangunan model ARIMA, prediksi, dan mengevaluasi performa model. Hasil uji ADF menunjukkan bahwa data menjadi stasioner setelah differencing kedua. Berdasarkan hasil grid search dan nilai Akaike Information Criterion (AIC) yang paling rendah. Dengan nilai AIC, model yang paling cocok adalah ARIMA (1,2,2). Sebesar 2188,98. Evaluasi terhadap nilai Mean Absolute Percentage Error (MAPE) sebesar 8,90%, Root Mean Squared Error (RMSE) sebesar 636,97 dan Mean Squared Error (MSE) sebesar 405731,65. Dari hasil penelitian ini, dapat disimpulkan bahwa model ARIMA cukup andal dan akurat dalam memprediksi harga gabah kering panen.
Pendekatan Backpropagation Artificial Neural Network Untuk Prediksi Kemurnian Madu Tafsir, Andi Muh Ihsanul; Sulfayanti, Sulfayanti; Nur, Nahya
Techno.Com Vol. 24 No. 4 (2025): November 2025
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v24i4.14855

Abstract

Madu merupakan produk alami yang kemurniannya menjadi indikator utama kualitas dan keasliannya. Penelitian ini bertujuan untuk memprediksi tingkat kemurnian madu menggunakan algoritma Artificial Neural Network (ANN) dengan metode Backpropagation. Dataset terdiri dari 247.903 data dengan 10 atribut madu yang digunakan sebagai variabel input, sedangkan tingkat kemurnian madu dijadikan sebagai target output. Tahapan penelitian meliputi pra-pemrosesan data, pelatihan model, serta evaluasi hasil prediksi. Setelah melalui tahap pra-pemrosesan, jumlah fitur input bertambah menjadi 27. Pada proses eksperimen, dilakukan pengujian beberapa variasi arsitektur (27-14-14-1, 27-27-27-1, 27-54-54-1), fungsi aktivasi (ReLU, sigmoid biner, sigmoid bipolar), learning rate (0,01, 0,1, 0,5), dan jumlah epoch (1000, 1500, 2000) untuk memperoleh konfigurasi terbaik. Hasil optimal diperoleh pada arsitektur jaringan 27-54-54-1 dengan fungsi aktivasi ReLU, learning rate 0,5, dan jumlah epoch sebanyak 2000. Konfigurasi tersebut menghasilkan kinerja prediksi dengan nilai Mean Squared Error (MSE) 0,000542, R-squared (R²) sebesar 0,972010, dan Mean Absolute Percentage Error (MAPE) 1,26%. Hasil ini membuktikan bahwa algoritma Backpropagation Artificial Neural Network dapat digunakan secara efektif dalam memprediksi tingkat kemurnian madu. Kata Kunci - Artificial Neural Network, Backpropagation, Prediksi, Kemurnian Madu
Co-Authors -, Ika Nurul Fadhillah Ade Mulawarman Agus Zainal Arifin Alifia Puspaningrum Ardi, Rahmawati Arfiah, Sitti Arnita Irianti Asmawati Asnan Cirua, A. Amirul Aulia, Novita Dian Megah Sari Dian Megah Sari Faharuddin Situju, Sulfayanti Farid Wajidi Fitriani Fitriani Halal, Nursan Heliawaty Hamrul Heliawaty Hamrul, Heliawaty Heri, Adi Iin Aisyah Khofifah Indra Indra Indra Indra Iqram, Muh. Irianti, Arnita Ismaun Muh. Adrian mustafa, mukhdar Nur Syahra Nurdina Rasjid Nurul Inayah Putri, Haerya Quraisy, Muh. Imam Rasjid, Nurdina Rensa, Erma Ria, Canda Rian, Andrian Rifky Gunawan Riza, Ozzy Secio Situju, Sulfayanti Sugiarto Cokrowibowo Sulfayanti Sulfayanti Sulfayanti, Sulfayanti Syarkia Yunus Tafsir, Andi Muh Ihsanul wahyuni wahyuni rahman Wildayani Wildayani