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All Journal Sainteks Jurnal Ilmiah Ekonomi Islam Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Justek : Jurnal Sains Dan Teknologi NUSANTARA : Jurnal Ilmu Pengetahuan Sosial CYBERNETICS BULETIN AL-RIBAATH GERVASI: Jurnal Pengabdian kepada Masyarakat Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Progresif: Jurnal Ilmiah Komputer Journal of Innovation Information Technology and Application (JINITA) JUTECH : Journal Education and Technology Jurnal Media Informatika Jurnal Nasional Teknik Elektro dan Teknologi Informasi Journal of Artificial Intelligence and Engineering Applications (JAIEA) SmartComp Jurnal Komputer, Informasi dan Teknologi TEKNOSAINS: Jurnal Sains, Teknologi dan Informatika
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Teknik Informatika Universitas Muhammadiyah Pontianak
Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Social Sciences Other

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Rancang Bangun Sistem Informasi Pada Kantor Desa Sebagai Media Pengajuan Surat Dengan Metode Waterfall Dunga Triandri; Istikoma; Sucipto
Jurnal Komputer, Informasi dan Teknologi Vol. 5 No. 1 (2025): Juni
Publisher : Penerbit Jurnal Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53697/jkomitek.v5i1.2375

Abstract

The Tumuk Manggis Village Government, Sambas District, Sambas Regency still provides manual letter processing services, which require people to come directly to the Village office. This is considered inefficient because the process takes a long time, coupled with the lack of human resources and mastery of technology by Village employees. In addition, on average, employees at the village office are rarely there and also operational hours are not opened according to the adjusted time. To overcome this problem, a website-based information system is needed that can facilitate online letter submission. This study aims to design and build a letter submission information system using the waterfall method, which includes the stages of needs analysis, design, implementation, testing, and maintenance. The waterfall method was chosen because the process is structured and carried out in stages so that it is easy to develop the system according to what the user wants. Testing was carried out using black box testing to ensure that the system being built is seen and confirmed. The results of the UAT test obtained with an average value of 90.2% which is included in the Strongly Agree category. The results of this study are a website-based system that is expected to be able to accelerate and streamline the letter submission process, so that services become more structured and effective for the people of Tumuk Manggis Village
Penerapan Data Mining untuk Klasifikasi Tingkat Kepuasan Pelanggan Café Menggunakan Metode Decision Tree C4.5 Atta Tha Ariq; Sucipto Sucipto; Rachmat Wahid Saleh Insani
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 15, No 1 (2026): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v15i1.9653

Abstract

Kepuasan pelanggan adalah faktor utama dalam meningkatkan reputasi bisnis, loyalitas pelanggan, dan efisiensi operasional. Penelitian ini bertujuan mengembangkan sistem yang memberikan informasi akurat tentang tingkat kepuasan dan ketidakpuasan pelanggan di sebuah café. Harapannya, temuan dari penelitian ini dapat memberikan dampak baik kepada café guna untuk meningkatkan kualitas pelayanan dengan mengetahui apa saja indikatot-indikator yang mempengaruhi tingkat kepuasan pelanggan café. Metode yang digunakan adalah Decision Tree C4.5, yang membangun pohon keputusan untuk klasifikasi. Proses meliputi penanganan missing value, pengecekan duplicate data, label encoding, penanganan data imbalance dengan SMOTE, pemodelan Decision Tree C4.5, pengecekan akurasi, dan visualisasi aturan keputusan. Evaluasi model dilakukan menggunakan metrik confusion matrix. Hasil evaluasi menunjukkan bahwa model klasifikasi memiliki performa sangat baik, dengan accuracy 98% pada data latih dan 93% pada data uji. Nilai recall, precision, dan F1-score masing-masing adalah 94%, 97%, dan 95%.
Comparison of Naive Bayes and KNN Algorithms for Heart Attack Disease Classification Syahril Arsad; Sucipto; Barry Caesar Octariadi
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2218

Abstract

This Heart attack is one of the leading causes of death worldwide and requires early diagnosis to reduce fatal risks. This study aims to compare the performance of the Naive Bayes and K-Nearest Neighbors (KNN) algorithms in classifying heart attack disease. The dataset used consists of medical records containing clinical parameters such as age, blood pressure, cholesterol level, and heart rate. The research methodology includes data preprocessing, splitting the dataset into training and testing sets, and evaluating performance using accuracy, precision, recall, and F1-score metrics. The results show that Naive Bayes demonstrates advantages in computational speed and performs well on smaller datasets, achieving an accuracy of 85%. In contrast, KNN provides better performance on larger datasets, reaching an accuracy of 90%, particularly when the optimal K value is applied. These findings indicate that algorithm selection for heart attack classification depends on dataset characteristics and specific implementation needs. This study is expected to contribute to the development of artificial intelligence–based clinical decision support systems for early heart attack diagnosis and improved healthcare outcomes.
Implementation of a Web-Based Decision Support System for New Employee Recruitment Using the VIKOR Method Arochman; Sucipto; Asrul Abdullah
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2298

Abstract

An effective and objective employee selection process is essential to obtain high-quality human resources. This study aims to develop a web-based decision support system to assist in the recruitment of new employees using the VIKOR method. The VIKOR method is chosen because it can rank alternatives based on their closeness to the ideal solution while considering compromise among criteria. The criteria used in the system include education, work experience, skills, interview results, and work personality. This research adopts the waterfall approach for system development and implements PHP programming language with a MySQL database. The testing results indicate that the system is capable of providing accurate and consistent rankings of job candidates, as well as facilitating the HR team in conducting evaluations more efficiently.
Network Device Performance Monitoring Using the Simple Network Management Protocol (SNMP) Method Aldi Mulia Rismanto; Asrul Abdullah; Sucipto
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2346

Abstract

Network problems frequently occur at Politeknik Negeri Pontianak due to the increasing number and scale of network devices. These issues require continuous monitoring to ensure service availability across all network devices. To address this problem, the author conducted network monitoring using the SNMP (Simple Network Management Protocol) method and network performance measurement using the Wireshark application. SNMP is a standard protocol used to monitor and manage network devices such as routers, switches, servers, and other networking equipment. The research stages began with data collection, followed by monitoring and performance testing of the network. After testing the network in the Informatics Engineering Building, both satisfactory and unsatisfactory results were obtained. The results of SNMP measurements on MRTG showed the lowest throughput values on the second day of testing, with 485.6 kbps for daily traffic, 236.8 kbps for weekly traffic, 232 kbps for monthly traffic, and 121.6 kbps for yearly traffic. Meanwhile, the Quality of Service measurement produced the lowest throughput value of 0.225 kbps, packet loss of 0.354%, delay of 3.331 ms, and jitter of 8.763 ms.
Rice Planting Time Prediction Using SARIMA-MFEP Integration in Kubu Raya Sinta Rama Dani; Syarifah Putri Agustini Alkadri; Sucipto
Journal of Innovation Information Technology and Application (JINITA) Vol 8 No 1 (2026): JINITA, June 2026
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v8i1.3233

Abstract

Extreme climate change has increased uncertainty in rice planting schedules, threatening food security in Kubu Raya Regency, West Kalimantan, and causing significant economic losses due to inaccurate seasonal predictions. This study integrates the Seasonal Autoregressive Integrated Moving Average (SARIMA) method with the Multi-Factor Evaluation Process (MFEP) to generate rice planting time recommendations based on scientific climate forecasting and multi-criteria agroclimatic evaluation. SARIMA is employed to forecast monthly rainfall, temperature, and humidity, while MFEP evaluates the feasibility of twelve alternative planting months using weighted criteria determined by local agricultural experts. The objective of this research is to develop an objective, accurate, and validated planting time prediction system to support farmers’ decision-making. The results show that the SARIMA model achieves very high accuracy, with Mean Absolute Percentage Error (MAPE) values below 2% for both temperature and humidity, and successfully captures 68% of seasonal rainfall variability. October is identified as the optimal planting month with the highest feasibility score, consistent with historical peak harvest patterns in January and February and aligned with regional literature. This integrated approach provides an end-to-end solution from forecasting to empirically validated, actionable recommendations, offering strong potential to reduce crop failure risk and enhance rice production efficiency under climate uncertainty.
Prediction of the level of crime cases using multiple linear regression in the city of Pontianak Fadillah Bergas; Sucipto; Asrul Abdullah
TEKNOSAINS : Jurnal Sains, Teknologi dan Informatika Vol 11 No 2 (2024): TEKNOSAINS: Jurnal Sains, Teknologi dan Informatika
Publisher : LPPMPK- Universitas Muhammadiyah Cileungsi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37373/tekno.v11i2.1025

Abstract

This study aims to develop a predictive model for the crime rate in the Police Resort Area of Kota (POLRESTA) Pontianak using the Multiple Linear Regression method based on secondary data obtained from the Criminal Investigation Unit of POLRESTA Pontianak. The utilization of descriptive statistical techniques and data visualization aids in identifying relevant features that enrich the information within the model. The evaluation results indicate that this model performs well in both modeling and predicting crime rates in Kota Pontianak. Despite the variations in error rates between training and testing data, the model still demonstrates its proficiency in predicting known data. The testing results also reveal that the Mean Absolute Percentage Error (MAPE) values for each crime category exhibit variations in the testing dataset, with MAPE for "Berat" increasing to 12.91%, MAPE for "Sedang" increasing to 30.11%, and MAPE for "Ringan" increasing to 26.59%. Consequently, this study concludes that the Multiple Linear Regression method holds potential as an effective tool for decision-making and the development of strategies to combat criminal activities in Kota Pontianak
PERBANDINGAN ALGORITMA RANDOM FOREST REGGRESSOR DAN SUPPORT VECTOR MACHINE DALAM PREDIKSI HARGA RUMAH DI KALIMANTAN BARAT Gebby Gisela; Sucipto Sucipto
NUSANTARA : Jurnal Ilmu Pengetahuan Sosial Vol 13, No 7 (2026): NUSANTARA : Jurnal Ilmu Pengetahuan Sosial
Publisher : Universitas Muhammadiyah Tapanuli Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31604/jips.v13i7.2026.1865-1871

Abstract

Pertumbuhan industri otomotif di Indonesia turut mendorong tingginya permintaan terhadap mobil bekas sebagai alternatif yang lebih ekonomis dibandingkan mobil baru. Namun, penentuan harga mobil bekas sering kali menjadi tantangan bagi showroom maupun calon pembeli karena melibatkan banyak faktor dan bersifat subjektif. Penelitian ini bertujuan untuk membangun model prediksi harga mobil bekas menggunakan algoritma Support Vector Regression (SVR) dengan pendekatan Radial Basis Function (RBF) kernel. Data sebanyak 1.000 entri diperoleh melalui teknik web scraping dari situs cintamobil.com. Metodologi penelitian mengacu pada kerangka kerja CRISP-DM, dimulai dari pemahaman bisnis hingga deployment model melalui aplikasi web menggunakan Streamlit. Proses preprocessing melibatkan penanganan missing value, outlier, duplikasi data, serta transformasi fitur numerik dan kategorikal. Model SVR dievaluasi menggunakan metrik RMSE, MAPE, dan MAE untuk menilai akurasi prediksi. Hasil penelitian menunjukkan bahwa SVR mampu memberikan prediksi harga yang cukup akurat, dengan parameter C=1, gamma=0.1, dan epsilon=0.1 yang menghasilkan performa terbaik, yaitu nilai MAE sebesar Rp 6.472.572, RMSE sebesar Rp 8.958.555, dan MAPE sebesar 3,41%. Mengacu pada kategori tingkat akurasi prediksi berdasarkan nilai MAPE, di mana nilai MAPE ? 10% dikategorikan sebagai akurasi tinggi, maka model ini dapat disimpulkan memiliki akurasi prediksi yang tinggi. Hal ini menunjukkan bahwa model SVR yang digunakan mampu memperkirakan harga mobil bekas dengan tingkat kesalahan yang rendah dan ketepatan yang baik.
PREDIKSI PANEN KELAPA SAWIT MENGGUNAKAN METODE RANDOM FOREST REGRESSION Friska Dwi Choirunisa; Menur Wahyu Pangestika; Sucipto Sucipto
NUSANTARA : Jurnal Ilmu Pengetahuan Sosial Vol 13, No 7 (2026): NUSANTARA : Jurnal Ilmu Pengetahuan Sosial
Publisher : Universitas Muhammadiyah Tapanuli Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31604/jips.v13i7.2026.1984-1989

Abstract

Tanaman kelapa sawit termasuk dalam komoditas perkebunan unggulan yang memiliki kontribusi signifikan terhadap pertumbuhan ekonomi, sehingga dibutuhkan strategi perencanaan panen yang tepat guna mengoptimalkan pemanfaatan sumber daya yang tersedia. Kelompok Tani "ASRI" hingga saat ini belum memiliki sistem yang mampu memperkirakan hasil panen secara tepat, sehingga proses perencanaan masih bergantung pada cara konvensional. Penelitian ini bertujuan membangun sebuah sistem prediksi hasil panen kelapa sawit dengan menggunakan metode Random Forest Regression yang berlandaskan data historis panen. Variabel yang digunakan mencakup tahun panen, bulan panen, usia tanaman, jenis tanah, pemberian pupuk, kondisi musim, dan luas areal tanam. Alur penelitian meliputi pengumpulan data, tahap preprocessing yang terdiri dari pembersihan data, penanganan data pencilan, normalisasi, dan label encoding, kemudian dilanjutkan dengan pemisahan data menjadi data latih dan data uji, pelatihan model Random Forest Regression, serta evaluasi kinerja model menggunakan Mean Absolute Error (MAE) dan Root Mean Squared Error (RMSE). Berdasarkan hasil pengujian, model menghasilkan nilai MAE sebesar 869,75 kg/bulan dan RMSE sebesar 1111,89 kg/bulan, yang menunjukkan bahwa model mampu menghasilkan prediksi hasil panen dengan tingkat deviasi yang tergolong rendah. Model yang telah dikembangkan selanjutnya diimplementasikan dalam bentuk aplikasi berbasis Streamlit sebagai alat bantu dalam perencanaan panen dan proses pengambilan keputusan di Kelompok Tani "ASRI".
PENERAPAN ALGORITMA TF-IDF DAN COSINE SIMILARITY UNTUK PENCARIAN ARSIP DOKUMEN DI KANTOR DESA KELAKAR Ria Maisya Syarifah; Menur Wahyu Pangestika; Sucipto Sucipto
NUSANTARA : Jurnal Ilmu Pengetahuan Sosial Vol 13, No 7 (2026): NUSANTARA : Jurnal Ilmu Pengetahuan Sosial
Publisher : Universitas Muhammadiyah Tapanuli Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31604/jips.v13i7.2026.2019-2024

Abstract

Pengelolaan arsip dokumen di Kantor Desa Kelakar hingga saat ini masih dilakukan tanpa sistem digital, sehingga menyebabkan kesulitan dalam pencarian dokumen, risiko kehilangan arsip, serta pemborosan waktu dan tenaga. Seiring meningkatnya kebutuhan akan pengelolaan arsip yang cepat dan akurat di Kantor Desa Kelakar, diperlukan sebuah sistem pencarian arsip digital yang mampu menampilkan dokumen secara relevan berdasarkan kata kunci pencarian. Penelitian ini menggunakan data berupa arsip dokumen administrasi desa dalam bentuk file PDF dan DOCX, termasuk dokumen hasil pemindaian yang diekstraksi menggunakan teknologi Optical Character Recognition (OCR). Tujuan penelitian ini adalah menerapkan algoritma Term Frequency–Inverse Document Frequency (TF-IDF) dan Cosine Similarity dalam sistem pencarian arsip dokumen berbasis web di Kantor Desa Kelakar. Hasil penelitian menunjukkan bahwa sistem mampu menampilkan dokumen yang relevan sesuai dengan kata kunci pencarian serta mempercepat proses temu kembali arsip. Hasil pengujian akurasi sistem menunjukkan nilai precision sebesar 0,001, recall sebesar 1,0, dan F1-score sebesar 0,0020, yang menunjukkan bahwa sistem mampu menampilkan dokumen yang relevan sesuai dengan kata kunci yang dimasukkan oleh pengguna. Selain itu, hasil white box testing menunjukkan bahwa seluruh fungsi dan alur logika program berjalan sesuai dengan rancangan yang telah ditetapkan tanpa ditemukan kesalahan fungsional yang signifikan. Dengan demikian, penerapan algoritma TF-IDF dan Cosine Similarity pada sistem pencarian arsip dokumen ini terbukti efektif dalam meningkatkan efisiensi dan kualitas pengelolaan arsip di Kantor Desa Kelakar
Co-Authors Aldi Mulia Rismanto Alya Amanda Putri Arfiyan, Fandika Roinaldi Arochman Asrul Abdullah Atta Tha Ariq Barry Caesar Octariadi Barry Ceasar Octariadi Dedy Susanto Diky Pratama Dunga Triandri Fadillah Bergas Fenni Supriadi Friska Dwi Choirunisa Gebby Gisela Istikoma Khorlis Jainudin M. Khairul Anwari Menur Wahyu Pangestika, Menur Wahyu Muhammad Hendri Rachmat Wahid Saleh Insani Rachmat Wahid Saleh Insani Ria Maisya Syarifah Sinta Rama Dani Sri Wahyuni Syahril Arsad Syarifah Putri Agustini Alkadri Ya' Ilham Eka Saputra Yulrio Brianorman