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Rendy Amy Saputra
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SISFOTENIKA
Published by Sekolah Tinggi Manajemen Informatika dan Komputer Pontianak
ISSN : 20877897     EISSN : 24605344     DOI : http://dx.doi.org/10.30700
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering
Jurnal Ilmiah SISFOTENIKAditerbitkan oleh LPPM STMIK Pontianak dan IndoCEISS. Frekuensi Terbit Tengah Tahunan (2 kali dalam setahun, yaitu bulan Januari dan Juli). Jurnal SISFOTENIKA terakreditasi SINTA 4 melalui Surat Keputusan Direktur Jenderal Pendidikan Tinggi, Riset, dan Teknologi Kementerian Pendidikan Kebudayaan, Riset dan Teknologi Nomor 5162/E4/AK.04/2021, tanggal 27 Desember 2021 tentang Pemberitahuan Hasil Akreditasi Jurnal Ilmiah Periode I Tahun 2021. Jurnal SISFOTENIKA Reakreditasi Tetap di Peringkat 4 mulai Volume 11 Nomor 1 Tahun 2021 sampai Volume 15 Nomor 2 Tahun 2025. Topik yang akan dipublikasikan oleh Jurnal Ilmiah SISFOTENIKA berhubungan dengan teknologi informasi, komunikasi dan komputer yang berbentuk kumpulan/akumulasi pengetahuan baru, pengamatan empirik atau hasil penelitian, dan pengembangan gagasan atau usulan baru. Jurnal Ilmiah SISFOTENIKA merupakan Jurnal Keilmuan bidang Sistem Informasi dan Teknologi Informasi yang memuat tulisan-tulisan ilmiah mengenai penelitian-penelitian murni dan terapan di bidang Sistem Informasi dan Teknologi Informasi serta penerapan ilmu di bidang terkait lainnya. Pada setiap terbitannya, tulisan yang dimuat pada jurnal ini oleh penulis baik dari STMIK Pontianak maupun penulis dari kampus-kampus lain yang ada di Kalimantan Barat ataupun dari luar daerah yang menyumbangkan tulisannya untuk diterbitkan pada Jurnal Ilmiah SISFOTENIKA ini.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 39 Documents
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Pengembangan Aplikasi Pencarian Pekerjaan Part-Time, Proyek, dan Penelitian bagi Mahasiswa Berbasis Website Amru Abid Zakly; Aisyah Fadzila Hani; Muhammad Nailan Nabil; Rahmadi Wijaya; Erna Hikmawati
SISFOTENIKA Vol. 14 No. 2 (2024): SISFOTENIKA
Publisher : STMIK PONTIANAK

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Abstract

Unemployment among undergraduate is a serious problem that has a broad impact on individuals and society. The lack of a platform that can help students gain experience and understand trends in the industrial world is one of the factors causing the high unemployment rate among undergraduates. To overcome this problem, this research developed the Boedoo application, a website-based platform that facilitates searching for part-time jobs, projects and research for students. This application was developed using the Software Development Life Cycle (SDLC) method with the Waterfall model. Through User Acceptance Testing (UAT) involving 10 Telkom University students, the Boedoo application succeeded in showing its effectiveness, with 89.6% of respondents agreeing that the existing features had been successfully implemented well. The results of this research show that Boedoo can help students gain hands-on experience in the industry, increase user engagement and satisfaction, and facilitate their transition into the job market. In this way, Boedoo contributes to reducing the unemployment rate of undergraduate graduates and increasing their competitiveness in the world of work.
Rekomendasi Paket Pakaian Berdasarkan Pola Penjualan Menggunakan Algoritma Apriori Agnes Eka Noviyanti; Safitri Juanita
SISFOTENIKA Vol. 14 No. 2 (2024): SISFOTENIKA
Publisher : STMIK PONTIANAK

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Abstract

The global economy in 2023 is predicted to experience a recession along with declining activity in the trade sector in most countries in the world, including Indonesia. Currently, one of the retail clothing stores that provides many clothing products for women, namely Toko Alys Studio, wants to develop a sales strategy for clothing products in order to compete and increase profits. Thus, the contribution of this research is to find patterns of products sold based on a collection of sales transaction data at Toko Alys Studio using association rules with the Apriori algorithm. The purpose of this research is to provide recommendations for clothing sales patterns so that they can be used to develop the right clothing product sales strategy in order to get more significant profits than before. The data mining methodology used is CRISPM-DM, using a dataset of store sales transaction records from June to September 2021, totaling 885 data, pre-processing to modeling with the Apriori algorithm, and all processes using WEKA 3.8. This study concludes that using the Apriori algorithm with a minimum Support value of 15% and a minimum Confidence value of 50%, successfully found the best pattern recommendations for the sale of clothing products at Toko Alys Studio, namely a combination of 2 products as many as 10 types of patterns, and a combination of 3 products with 3 main types of patterns.
Prediksi Kualitas Susu Menggunakan Metode K-Nearest Neighbors Nazori Suhandi; Rendra Gustriansyah; Abel Destria; Marshanda Amalia; Via Kris
SISFOTENIKA Vol. 14 No. 2 (2024): SISFOTENIKA
Publisher : STMIK PONTIANAK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30700/sisfotenika.v14i2.430

Abstract

Milk is a nutrient-rich source abundant in calcium and lactose, playing a crucial role in addressing nutritional deficiencies. Milk quality is determined by pH levels and pasteurization processes. This research aims to predict milk quality using the K-Nearest Neighbors (K-NN) Method. The analysis is conducted through a series of steps, including data preprocessing involving categorical data encoding, handling missing values, and data cleansing. Subsequently, the optimal K value is selected using the elbow method, with a value of K=3. The data is then divided into training and testing sets to avoid overfitting and validate model performance, and the testing results of using K-NN to predict milk quality are evaluated using three different data splitting schemes: 80-20, 70-30, and 60-40. By utilizing Confusion Matrix to calculate precision, recall, and accuracy, we can assess the proportion of correctly classified positive cases, accurately identified. The best accuracy result is obtained from scheme one at 0,94, with a recall of 0.8, and precision reaching 1. This research provides a significant contribution to understanding, predicting, and monitoring milk quality, encompassing a profound understanding of factors influencing milk quality and the development of advanced predictive models. Overall, this study strengthens the scientific foundation for the dairy industry comprehensively.
Desain Sistem Pariwisata Banyuwangi Menggunakan Pendekatan Atomic Desain dan Block Element Modifier Arif Hadi Sumitro; Yoyon Arie Budi Suprio
SISFOTENIKA Vol. 14 No. 2 (2024): SISFOTENIKA
Publisher : STMIK PONTIANAK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30700/sisfotenika.v14i2.443

Abstract

Dalam menyajikan suatu informasi yang baik perlu memahami beberapa hal dalam penyampaiannya. Salah satu hal yang menjadikan informasi tersampaikan adalah adanya data dan tampilan yang disajikan dengan baik atas informasi tersebut. Hal ini berkaitan erat dengan informasi mengenai pariwisata yang ada dibanyuwangi. Informasi yang disajikan melalui media website memerlukan cara penyajian yang menarik. terutama mengenai design sistemnya. Urgensi disini dikarenakan system informasi website yang dibuat tidak memiliki design system yang standar. Selama ini design yang ada hanya sebatas umum serta tidak dipersiapkan untuk pengembangan lebih lanjut. Sehingga dengan adanya design system ini akan mempermudah dalam pengembangan website dengan tema yang sama. Design system yang telah dianalisa yaitu menyajikan data bukan hanya bentuk desainnya, namun juga lebih ke dokumentasi library dari model desain dan juga penamaan komponen desain menggunakan model BEM(Block Element Modifier). Sehingga untuk disetiap library atau kode yang dibuat akan mempermudah developer selanjutnya dalam mengembangkan design system pariwisata di Banyuwangi. Selain itu, untuk mempermudah dalam penempatan informasi sesuai dengan system, juga diberikan beberapa contoh penggunaan komponen design menggunakan pendekatan atomic desain. Sehingga data komponen yang disajikan bukan hanya dalam bentuk satu komponen, namun juga gabungan dari beberapa komponen. Dan dari hasil pengujian yang dilakukan, 46% developer bisa lebih cepat dalam pengembangan lanjutan.
Sistem Rekomendasi dan Peminjaman Buku Menggunakan Algoritma Hybrid Based Filtering Lily Aprilyani; Natalis Ransi; Rizal Adi Saputra; Isnawaty Isnawaty
SISFOTENIKA Vol. 14 No. 2 (2024): SISFOTENIKA
Publisher : STMIK PONTIANAK

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Abstract

SMAN 1 Ladongi in East Kolaka has gained a reputation as an educational institution that is highly committed in providing quality education for the younger generation. SMA Negeri 1 Ladongi in East Kolaka Regency has a diverse collection of books, but the library service is still done manually. This process is not only time-consuming, but also less efficient, especially considering the large number of book collections. Implementing an online book recommendation and lending system can be a solution to improve the effectiveness and efficiency of library services. With a mobile web-based system, students and library staff can easily access book recommendations and perform the loan process online. This research uses a Hybrid Recommendation System that combines Item-Based Collaborative Filtering and User-Based Collaborative Filtering methods. The combination of these two methods aims to obtain better recommendation results. Based on the results of testing the accuracy of the recommendations that have been carried out, it is obtained for the MAE value of 4.52, then the MSE value is 0.02 and for the MAPE value obtained is 0.76%.
Sistem Rekomendasi Produk UMKM Menggunakan Algoritma User-Based Collaborative Filtering Berbasis Website Esy Anugerah Rahayu Kasim; Statiswaty Statiswaty; Natalis Ransi; Isnawaty Isnawaty
SISFOTENIKA Vol. 14 No. 2 (2024): SISFOTENIKA
Publisher : STMIK PONTIANAK

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Abstract

Kolaka Regency is one of the regions in Southeast Sulawesi Province that is rich in quality MSME products, but faces obstacles in promoting these products to a wider market. This research aims to adapt and optimise the UBCF method for the Kolaka MSME context and improve the algorithm to overcome the challenges faced, especially data limitations and variations in customer preferences that change quickly. This research method uses a recommendation engine approach with the UBCF method, which is applied through the stages of data preparation, UBCF implementation on a web-based system, and recommendation accuracy testing. The data used is product rating data from users, which is then processed using UBCF. The test results show that this recommendation system is able to provide fairly accurate rating predictions. Based on the results of testing the accuracy of the recommendations that have been carried out, it is obtained for the MAE value of 1.11, then the MSE value of 0.0649 and for the MAPE value obtained of 1.65%. This research contributes to improving the competitiveness of MSME products in Kolaka through UBCF technology, and provides a model that can be applied in other areas with similar characteristics.
Simulasi Metode PID Pada Motor Pengaduk Cairan Nacl Berbasis PLC Ageng Rochmad Joko Purwoko; Imam Sutrisno; Lilik Subiyanto; Isa Rachman; Muhammad Khoirul Hasin; Dwi Sasmita Aji Pambudi
SISFOTENIKA Vol. 14 No. 2 (2024): SISFOTENIKA
Publisher : STMIK PONTIANAK

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Abstract

AC motors are becoming increasingly popular in the industry due to their reliability and ease of control. It can be used in liquid mixing machines that require regular mixing of materials. The aim of this research is to control the speed of an AC motor using the PID method to ensure that the motor rotation remains stable when a disturbance occurs. The simulation results show that applying PID parameters with Kp of 4717, Ki of 41059, and Kd of 0.136 produces an error value of 8.75%. This is different from a system without PID, which has an error value of 15.2%, and these results show a performance increase of 6.45%. Therefore, the PID method is effective in improving the performance of PLC-based liquid stirring systems
Implementasi Support Vector Machine (SVM) pada Klasifikasi Jenis Tanah Memanfaatkan Fitur RGB Maulana Feri Setyawan; Jaemsyien Devgan Oktawijaya; Soffiana Agustin
SISFOTENIKA Vol. 14 No. 2 (2024): SISFOTENIKA
Publisher : STMIK PONTIANAK

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Abstract

Each type of soil possesses unique characteristics that influence various aspects of human life and activities, particularly in agriculture, construction, and ecology. Naturally, soil exhibits distinct colors that aid human interpretation in identifying soil types. However, these soil types are not widely known. Understanding soil types can facilitate better decision-making across various aspects of life and contribute to environmental conservation and natural resource management. Given the diversity of soil types with varying properties, tailored treatments for each type are essential. Therefore, soil classification is crucial for understanding effective soil management practices. The Support Vector Machine (SVM) algorithm excels in handling high-dimensional data, separating non-linear classes, and enhancing model accuracy and generalization, making it a robust choice for soil classification based on RGB (Red, Green, Blue) features. This research method involved collecting a personal dataset comprising various soil types, extracting RGB color features, and using first-order statistics for data representation. The results demonstrate that the optimized combination of RGB features and SVM achieved accurate and efficient soil classification with an accuracy of 88%.
Analisis Penerapan Fuzzy Tsukamoto dalam Penentuan Kelayakan Keikutsertaan Siswa pada SNMPTN Nur Rahmi; Lutfiah Tri Syahyaningsih; Warda Wahyuni; Dewi Fatmarani Surianto
SISFOTENIKA Vol. 14 No. 2 (2024): SISFOTENIKA
Publisher : STMIK PONTIANAK

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Abstract

This research implements the Fuzzy Tsukamoto method to determine the eligibility of MAN 1 Makassar City students in participating in the National Selection for State Universities (SNMPTN). The input variables used include knowledge scores, skill scores, achievements, and the number of alumni who passed the SNMPTN the previous year. Data is collected from students and school archives, then processed using fuzzy models to produce eligibility decisions. The fuzzy process includes fuzzification, inference, and defuzzification to transform input variables into concrete output values. The results showed that the Fuzzy Tsukamoto method was able to provide eligibility predictions with an accuracy rate of 76.6%. This finding confirms that the combination of knowledge, skills, achievement, and number of alumni graduated scores contribute significantly to student eligibility. This research makes an important contribution in the development of a more objective and accurate student eligibility evaluation system. In addition, the results of the study can be used as a basis for decision-making regarding the awarding of scholarships, awards, or student development programs.
2Deep Model Prediksi Berbasis Weighting Average Untuk Time Series Data Arwansyah; Cucut Susanto; Nurdiansah
SISFOTENIKA Vol. 14 No. 2 (2024): SISFOTENIKA
Publisher : STMIK PONTIANAK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30700/sisfotenika.v14i2.462

Abstract

In time series data analysis, the need for accurate and efficient predictive models is becoming increasingly urgent as data complexity rises. This study proposes the 2Deep Model, a hybrid approach that combines Bidirectional Long Short-Term Memory (Bi-LSTM) and Stacked LSTM, utilizing the Weighting Average technique to optimize predictions. This method was chosen for its potential in handling long-term dependencies and temporal complexity in data. Experiments were conducted on five datasets: ETTh1, ETTh2, ETTm1, ETTm2, and AQI Shanghai. The results show that the proposed model achieves low Mean Squared Error (MSE) and Mean Absolute Error (MAE) values on the first four datasets, with an average MSE of 0.0289 and an MAE of 0.0971, along with a relatively high R-squared (R²) value. However, for the AQI Shanghai dataset, the model's performance declined, with higher MSE and MAE values and a lower R². These findings indicate that the 2Deep Model holds significant potential for time series data prediction applications, although there is room for improvement when dealing with more diverse datasets. Future research suggestions include further model optimization and exploring other hybrid methods to enhance model generalization.

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