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Wibowo, Gentur Wahyu Nyipto
Universitas Islam Nahdlatul Ulama Jepara
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Analisis Kegunaan Aplikasi GoPay Berdasarkan Metode System Usability Scale Puji Sa’adah; Gentur Wahyu Nyipto Wibowo; R. Hadapiningradja Kusumodestoni
Jurnal Minfo Polgan Vol. 13 No. 1 (2024): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v13i1.13726

Abstract

Penelitian ini bertujuan untuk mengevaluasi pengalaman pengguna aplikasi GoPay dengan menggunakan metode System Usability Scale (SUS). GoPay adalah salah satu aplikasi e-wallet terkemuka di Indonesia yang digunakan untuk berbagai transaksi digital. Evaluasi usability penting untuk memastikan aplikasi ini memenuhi kebutuhan dan harapan penggunanya. Penelitian ini melibatkan 100 responden yang merupakan pengguna aktif GoPay. Data dikumpulkan melalui kuesioner SUS yang terdiri dari 10 pernyataan dengan skala Likert 5 poin. Hasil penelitian menunjukkan bahwa skor total SUS yang diperoleh dari 100 responden adalah 1.040. Dari perhitungan, rata-rata skor SUS adalah 10,4, yang setelah dikalikan dengan faktor skala SUS (2,5), menghasilkan skor rata-rata sebesar 26. Skor ini jauh di bawah ambang batas 68, yang dianggap sebagai skor minimal untuk kegunaan yang baik. Kesimpulan dari penelitian ini adalah bahwa aplikasi GoPay memiliki tingkat kegunaan yang sangat rendah, menunjukkan banyak area yang perlu diperbaiki dalam hal kemudahan penggunaan, efisiensi, dan kepuasan pengguna. Rekomendasi perbaikan meliputi evaluasi dan penyederhanaan antarmuka pengguna, peningkatan konsistensi desain, dan pengujian usability lebih lanjut secara berkala. Penelitian ini diharapkan dapat memberikan wawasan berharga bagi pengembang aplikasi GoPay dalam upaya meningkatkan pengalaman pengguna dan memastikan aplikasi memenuhi standar usability yang lebih tinggi.
Implementasi Algoritma K-Means untuk Klasterisasi Data Hasil Tangkapan Ikan di Karimunjawa Muchammad Dwi Rivaldo; Gentur Wahyu Nyipto Wibowo; Harminto Mulyo
Jurnal Minfo Polgan Vol. 13 No. 1 (2024): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v13i1.13928

Abstract

Penelitian ini berfokus pada implementasi algoritma K-Means untuk klasterisasi data hasil tangkapan ikan di Karimunjawa. Tujuan utama adalah untuk mengidentifikasi jenis ikan yang paling banyak ditangkap dan area penangkapan yang paling produktif. Data yang digunakan mencakup tanggal, nama nelayan, jumlah ikan (kg), jenis ikan, area penangkapan ikan, dan metode penangkapan ikan, yang dikumpulkan selama tahun 2020. Analisis klasterisasi menghasilkan tiga klaster utama: Klaster 0 dengan total 315,9 kg terdiri dari Cumi, Kakak Tua, Jinahak, Baronang, Panti, dan Tambak Jeron; Klaster 1 dengan total 856,9 kg terdiri dari Teri, Tengiri, Tambak Jeron, Udul, Panti, dan Pari; Klaster 2 dengan total 1383,2 kg terdiri dari Todak. Selain itu, area penangkapan yang produktif juga diklasterisasi menjadi tiga: Klaster 0 mencakup Karimunjawa Timur, Klaster 1 mencakup Karimunjawa Barat, dan Klaster 2 mencakup Karimunjawa Utara. Hasil evaluasi menggunakan metrik pengukuran menunjukkan bahwa Silhouette Score positif sebesar 0,48 mengindikasikan bahwa klaster yang dihasilkan cenderung terpisah dengan baik, meskipun masih ada ruang untuk perbaikan. Davies-Bouldin Index yang rendah sebesar 0,83 menunjukkan bahwa klaster yang dihasilkan cukup terpisah satu sama lain, meskipun tidak sempurna. Metode Elbow memberikan indikasi jumlah klaster optimal, membantu dalam pemilihan konfigurasi yang tepat untuk analisis klaster. Penelitian ini memberikan wawasan berharga tentang distribusi tangkapan ikan di Karimunjawa, yang dapat digunakan untuk meningkatkan strategi penangkapan dan manajemen perikanan. Implementasi dan analisis dilakukan menggunakan Google Colab dengan bahasa pemrograman Python.
Prediksi Ketuntasan Siswa Berbasis Data Multidimensi Menggunakan Metode K-Nearest Neighbor (KNN) di SMK NU Hasyim Asy'ari 2 Kudus Muhammad Syafi’ul Huda; Harminto Mulyo; Gentur Wahyu Nyipto Wibowo
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 10 No 4 (2026): OCTOBER 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v10i4.7015

Abstract

This research implements the K-Nearest Neighbors (KNN) algorithm to predict student learning mastery at SMK NU Hasyim Asy’ari 2 Kudus for the 2025/2026 academic year using multidimensional data. Following data preprocessing and labeling via median thresholding, the results indicate that the best performance is achieved at $K$ values of 7, 9, and 10, with an accuracy of 58.62%. While the precision of 0.69 demonstrates reasonable accuracy in predicting students who achieve mastery, the recall of 0.50 highlights the model's limitations in identifying all students who actually pass. These results are primarily influenced by the limited sample size and imbalanced class distribution. Overall, KNN serves as an effective initial approach for objective academic prediction, though further optimization through parameter tuning or feature engineering is required to enhance future accuracy.
Peningkatan Akurasi Prediksi Stok Bahan Baku Furnitur Menggunakan Algoritma Random Forest Regressor Berbasis Web Ahmad Nafi’uzzahidi; Gentur Wahyu Nyipto Wibowo; Sarwido Sarwido
Journal of Information System Research (JOSH) Vol 7 No 2 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i2.9095

Abstract

This study aims to address the uncertainty of raw material inventory in the furniture industry through the implementation of the Random Forest Regressor machine learning algorithm. The primary problem addressed is demand fluctuation, which frequently leads to stock management inefficiencies, including overstocking or material shortages that disrupt production processes. The research method employs a quantitative approach with an experimental design, developing a web-based system using the Flask framework and MySQL database. The data sample includes historical sales transaction records and Bill of Materials (BOM) data for furniture products, such as dining tables and minimalist chairs. Prior to modeling, the data underwent a preprocessing stage comprising data cleaning, handling missing values, and normalization to minimize the impact of noise on transaction data. Data collection was conducted through the extraction of internal databases, which were then processed through feature engineering stages based on temporal trends. The results demonstrate that the Random Forest model can predict future raw material requirements with high accuracy, evidenced by a coefficient of determination ($R^2$) of 0.84 and a Mean Absolute Error (MAE) of 5.4.5 These findings prove that a data-driven approach provides more precise stock requirement estimations than conventional methods. In conclusion, the integration of this predictive technology offers practical contributions to accelerating managerial decision-making and optimizing operational efficiency in the medium-scale manufacturing sector. The implications of this study support the theoretical development of artificial intelligence-based decision support systems in supply chain management.
Stunting Prediction in Toddlers Using the K-Nearest Neighbor (KNN) Method Based on a Web Application at Batealit Community Health Center, Jepara Lisa Falichatul Ibriza; Gentur Wahyu Nyipto Wibowo; Teguh Tamrin
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 3 (2025): DECEMBER 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i3.5553

Abstract

Stunting is still a nutritional problem that exists in Indonesia and it needs immediate intervention in Jepara Regency. At the primary healthcare level, Batealit Public Health Center uses manual anthropometric recording for toddlers' growth assessment. This method can be prone to human recording errors and operational delays which hinder prompt clinical decision-making. To improve this condition, this study develops a web-based system for predicting stunting based on the K-Nearest Neighbor (KNN) algorithm. The research method was applied research with system development using the Waterfall model by processing main variables such as age, weight, and height. We tested the algorithm intensively by trying different neighbor values (k) to obtain the maximum value for accuracy, precision, and recall. From experiments, the KNN algorithm is best at k=3 with a 95.23% accuracy rate; this configuration is better compared to larger k values since they increase misclassification rates on normal and stunted categories. By porting this logic into a web interface, detection moves from being a manual task to an automated one occurring in real-time thus application becomes an essential part of decision support enabling health workers to bypass administrative delays and find stunting much faster more accurately within Batealit service area.
Implementation of the Hybrid ARIMA-LSTM Model for Gold Price Prediction Based on Yahoo Finance Data Talitha Hananta Nurendasari; Gentur Wahyu Nyipto Wibowo; Harminto Mulyo
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 3 (2025): DECEMBER 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i3.5560

Abstract

This paper presents a hybrid ARIMA–LSTM model to forecast daily gold price using historical data from Yahoo Finance. Gold price is highly volatile due to macroeconomic, geopolitical, and monetary factors, making accurate forecasting difficult and increasing uncertainty in investment decisions. In this study, ARIMA is used for modeling linear patterns in the time series data, while an LSTM network captures the nonlinear relationships and temporal dynamics that are not captured by statistical models. The dataset consists of daily observations of gold prices between June 2022 and June 2025. The analysis involves cleaning and normalizing the data, splitting it into training and testing subsets, estimating ARIMA parameters, extracting residuals, and forecasting these residuals with LSTM. Performance evaluation is carried out through MAE, RMSE, and MAPE metrics. The hybrid framework compares favorably against standalone ARIMA and LSTM models in terms of all three metrics used for assessment. Empirical results show that the hybrid ARIMA–LSTM model produces lower forecasting errors than the individual models on all evaluation metrics. These findings validate that combining statistical time series modeling with neural sequence learning increases predictive reliability in volatile commodity markets. The proposed framework can be considered methodologically sound for gold price forecasting and subsequently may enhance informed decision-making within financial analysis as well as investment practice.
Implementasi Pemilah Sampah Otomatis Menggunakan ESP32-CAM dengan Metode Gaussian blur M. Ilham Sabilal Muna; Gentur Wahyu Nyipto Wibowo; Nadia Annisa Maori
Jurnal Pengembangan Teknologi Informasi dan Komunikasi (JUPTIK) Vol. 4 No. 1 (2026): JURNAL PENGEMBANGAN TEKNOLOGI INFORMASI DAN KOMUNIAKSI (JUPTIK)
Publisher : Universitas Muhammadiyah Muara Bungo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52060/juptik.v4i1.4186

Abstract

Pertambahan volum limbah padat secara eksponensial mengharuskan adanya transformasi dari sistem pengelolaan konvensional menuju mekanisme daur ulang. Pemilahan di titik sumber masih bergantung pada tenaga manual yang tidak efisien, termasuk di lingkungan SMK Hasan Kafrawi Mayong. Penelitian ini bertujuan mengimplementasikan prototipe pemilah sampah otomatis menggunakan modul ESP32-CAM yang dikombinasikan dengan metode Gaussian Blur sebagai filter spasial (low-pass filter) untuk meredam noise frekuensi tinggi dan glare akibat pencahayaan dinamis. Citra yang terfilter kemudian diklasifikasikan menggunakan thresholding warna HSV untuk menggerakkan aktuator servo. Hasil eksperimen menunjukkan tanpa Gaussian Blur, sistem hanya mencapai akurasi 35% akibat nilai HSV yang tidak stabil. Dengan penerapan tahap pra-pemrosesan Gaussian Blur, akurasi klasifikasi meningkat signifikan menjadi 85%, di mana sistem berhasil mengklasifikasikan 17 dari 20 sampel dengan benar. Meskipun proses filtrasi menambah beban komputasi sebesar 29 ms pada response time (dari 2062 ms menjadi 2091 ms), peningkatan tersebut relatif kecil dan tidak mengganggu operasi real-time perangkat.
Co-Authors Abdilllah, Muh Sajid Ah Dani Fian Cahya Ahmad Nafi’uzzahidi Akhmad Khanif Zyen, Akhmad Khanif Anis Sofiyati Anwari, Hilal Beni Ernando Charles Firmansyah Decky Rochmanto, Decky Dewantara, Rizki Faisti, Muhammad Jazum Fanida Muta’aliyur Rohmah Fathurrozi, Muhammad Gogor Christmass Setyawan Gun Sudiryanto Hanifah Nurul Muthmainah Hasan Ibrohim Haydar, Muhammad Rifqi Fajrul I Nengah Sandi Indriyani Indriyani Isyfa Fuhrotun Nadhifah Jeperson Hutahaean Joko Minardi, Joko Khasan Galuh Ramadhan Kraugusteeliana Kraugusteeliana Kusuma, Tahta Rias Tika C Laros Tuhuteru Lisa Falichatul Ibriza M. Ilham Sabilal Muna Ma'arif, Syamsul Maori, Nadia Annisa Mohammad, Gunawan Muchammad Dwi Rivaldo Muhammad Syafi’ul Huda Mulyo, Harminto Nadia Annisa Maori Nadia Annisa Maori Nafissatus Zahro Nanda, Meindita Andriano Nugroho Adi Prasetyo Nugroho, Muhammad Bayu Nur Aeni Widiastuti Nur Ali Nurofi, Muhammad Adib Puji Sa’adah R Hadapiningradja Kusumodestoni Rahma Dwi Ningsih Ricardus Anggi Pramunendar Rizky Alhusani Gifari Safrizal Safrizal Saludin Saludin Sarwido Sarwido Sarwido Sarwido Sarwido Siroj, Muhammad Nurus Sitorus, Lamhot Susatyo Adhi Pramono, Susatyo Adhi Syamsul Ma'arif Talitha Hananta Nurendasari Teddy Setiady Teguh Tamrin Tobias Duha Wenda, Alex Yatusifa, Cecilia Zaenal Arifin