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SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan
Published by RAM PUBLISHER
ISSN : 30901626     EISSN : 30323991     DOI : -
Core Subject : Humanities, Science, Engineering,
Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan or in English the publication title Information Systems, Engineering and Applied Technology is an open access journal committed to publishing high quality research articles in the fields of Information Systems, Informatics, Digital Communication Information Technology, Tourism Technology, Transportation Technology, Agricultural Technology, Plantations, Fisheries, Marine, Environmental Technology, Artificial Intelligence, Mechanical Engineering, Electrical Engineering, Industrial Engineering and Civil Engineering. Published 4 X (Times) a year in January, April, July, and October. SITEKNIK accepts and selects quality articles and focuses on providing the best service for writers. SITEKNIK is committed to being a leading platform for researchers to share their innovative findings. We also provide a fast and transparent review process to ensure the quality and originality of each published article.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 107 Documents
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COMPARISON ACCURACY OF C4.5 ALGORITHM AND K-NEAREST NEIGHBORS FOR RAINFALL CLASSIFICATION Muhammad Fauzan Nasrullah; RD. Rohmat Saedudin; Faqih Hamami
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 1 No. 2 (2024): July
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.14715070

Abstract

Indonesia has a predominantly tropical climate, hence Indonesia experiences limited temperature variations, but has diverse rainfall variations. The variability of rainfall is also inseparable from the impact it has on various aspects of human life and business activities. Therefore, rainfall information is an important aspect in decision making. However, of course, there are stages and methods needed to carry out the analysis process. Therefore, this study looked for the best method between C4.5 and K-Nearest Neighbors which included algorithms in data mining to classify rainfall data. Both algorithms are used to build classification models based on relevant attribute attributes. Then, testing and evaluating both models using various metrics such as Accuracy, Precision, Recall and F1-Score were carried out. In this study also applied Hyperparameter Tuning with the RandomizeSearchCV method to get the best parameters to get maximum accuracy values. The results showed good accuracy values for both algorithms, in the sense that both algorithms were able to classify rainfall based on Indonesia's climate well. Based on the accuracy values obtained with the default parameters of both algorithms, C4.5 produces a higher accuracy value of 81.42%, while K-Nearest Neighbors is only 78.10%. However, after using the best parameters resulting from the application of RandomizedSearchCV Hyperparameter Tuning, a significant change in accuracy value occurred in K-Nearest Neighbors which was found to be 83.37%, while C4.5 increased to 82.56%.
COMPARISON ANALYSIS OF RANDOM FOREST AND NAÏVE BAYES ALGORITHMS FORRAINFALL CLASSIFICATION BASED ON CLIMATE IN INDONESIA Nicolaus Advendea Prakoso Indaryono; RD. Rohmat Saedudin; Faqih Hamami
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 1 No. 2 (2024): July
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.14715081

Abstract

Indonesia predominantly features a tropical climate across its entirety. With this mostly tropical climate, the country encounters minimal shifts in temperature but exhibits a wide array of rainfall variations. Rainfall patterns in Indonesia showcase significant diversity. These variations in rainfall hold substantial importance in mitigating risks linked to heavy rainfall, such as floods and landslides. Moreover, besides its role in disaster preparedness, rainfall data also holds practical value in sectors such as agriculture, transportation, and industry. By incorporating data mining classification techniques, the process of predicting rainfall in Indonesia can be greatly enhanced. In this study, daily climate data from Indonesia is harnessed, and the chosen method for classification is the random forest algorithm. This selection stems from its capability to generate accurate and consistent classification models without necessitating intricate adjustments of parameters. Furthermore, the Naïve Bayes method is also integrated due to its straightforward implementation and its capacity for simple probability modeling, which can be effectively applied across diverse classification data. The outcomes of this investigation suggest that the random forest algorithm surpasses the Naïve Bayes algorithm in terms of performance and accuracy when classifying climate datasets unique to Indonesia. The random forest algorithm attains an accuracy rate of 86.55%, whereas the Naïve Bayes algorithm lags at an accuracy rate of 36.61%. It is anticipated that these research findings can serve as a point of reference for subsequent scholarly inquiries and contribute to the ongoing monitoring of daily rainfall in Indonesia, thereby aiding in the prevention of natural disasters.
PERBANDINGAN METODE PENJADWALAN DI UNIVERSITAS HASYIM ASY’ARI MENGGUNAKAN LOGIKA FUZZY MAMDANI DAN SUGENO TERHADAP ALGORITMA GENETIKA Much Zuyyinal Haqqul Barir; Fachrul Kurniawan; Sri Harini
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 1 No. 2 (2024): July
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.14715100

Abstract

Perencanaan merupakan suatu kegiatan yang harus dikuasai untuk membantu menyelesaikan aktivitas sehari-hari. Juga suatu instansi atau lembaga yang mempunyai tujuan penting yang harus dicapai secara rutin dan tekun. Begitu pentingnya waktu ini agar kegiatan dapat terlaksana sesuai rencana. Terlepas dari pentingnya penjadwalan ini, pembuatan jadwal ini merupakan proses yang sulit karena proses ini memerlukan banyak ketelitian dan waktu agar tidak terjadi tumpang tindih antar kegiatan. fase melarikan diri. Setiap tugas akhir harus mempunyai tahapan pengujian tugas akhir tersebut untuk memperoleh kelayakan memperoleh gelar sarjana. Optimasi secara umum adalah pencarian nilai terbaik dari beberapa fungsi berdasarkan konteksnya. “Optimal” dapat dikatakan berarti memilih unsur terbaik dari beberapa alternatif kalimat yang ada. Secara sederhana, ini dapat diartikan sebagai penyelesaian suatu masalah dengan meminimalkan atau memaksimalkan suatu fungsi dengan memilih secara sistematis nilai bilangan bulat atau variabel nyata dari yang diperbolehkan. Penerapan algoritma genetika menghasilkan penjadwalan yang baik ditinjau dari algoritma fuzzy mamdani dan sugeno, kedua fuzzy tersebut menyatakan bahwa hasil penjadwalan yang dibuat oleh algoritma genetika adalah baik, dengan mengikuti aturan yang ada, dengan menyatakan nilai keberhasilan jadwalnya ada pada nomor 8 dan 7 yang dibulatkan berurutan dari fuzzy mamdani dan sugeno.
Implementasi Business Intelligence Untuk Menganalisis Jumlah Mahasiswa Baru Tahun 2024 di Universitas Mercu Buana Yogyakarta Putry Wahyu Setyaningsih; Putri Taqwa Prasetyaningrum
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 2 No. 1 (2025): January
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.14756830

Abstract

Visualisasi data adalah representasi data dalam bentuk grafik, diagram, atau elemen visual lainnya yang memudahkan pemahaman pola, tren, dan informasi dari data tersebut. Visualisasi data membantu menyampaikan informasi kompleks secara lebih sederhana dan intuitif. Jumlah mahasiswa baru merupakan indikator penting dalam mengevaluasi kinerja dan daya saing suatu institusi pendidikan tinggi. Namun, analisis data jumlah mahasiswa baru seringkali menghadapi kendala, seperti kurangnya visualisasi data yang disajikan. Implementasi Business Intelligence (BI) menjadi solusi strategis untuk mengatasi masalah tersebut dengan menyediakan platform yang memungkinkan menyajikan visualisasi data secara efektif. Penelitian ini bertujuan untuk menerapkan BI dalam menganalisis jumlah mahasiswa baru. Alat yang digunakan dalam pengembangan BI meliputi perangkat lunak pengolahan data dan platform dashboard seperti Looker Studio. Hasil implementasi menunjukkan bahwa BI mampu memberikan wawasan yang lebih mendalam mengenai tren penerimaan mahasiswa baru. Dengan demikian, penerapan BI dapat meningkatkan efisiensi analisis data dan mendukung pengambilan keputusan strategis di institusi pendidikan tinggi.
Identifikasi Masalah dan Tantangan dalam Sistem Manajemen Pembelajaran (LMS) Berbasis Mobile di Pendidikan Tinggi Muharman Lubis; Rafian Ramadhani; Mochamad Yudha Febrianta
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 2 No. 1 (2025): January
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.14729075

Abstract

Penelitian ini bertujuan untuk menganalisis tantangan yang dihadapi dalam penerapan sistem manajemen pembelajaran berbasis mobile (m-LMS) di pendidikan tinggi dengan menggunakan metode literature review. Penelitian ini mengidentifikasi tantangan utama yang terbagi dalam tiga area utama: teknologi, pedagogis, dan organisasi. Hasil analisis menunjukkan bahwa keterbatasan infrastruktur seperti perangkat keras yang terbatas dan koneksi internet yang tidak stabil menjadi hambatan besar dalam implementasi m-LMS. Selain itu, faktor kualitas konten, desain aplikasi yang kurang responsif, serta rendahnya literasi digital di kalangan mahasiswa dan dosen turut mempengaruhi efektivitas penggunaan m-LMS. Penelitian ini menyarankan beberapa solusi, seperti pengoptimalan infrastruktur, peningkatan desain aplikasi, serta pelatihan intensif bagi mahasiswa dan dosen. Dengan dijabarkanya beberapa standarisasi maupum masalah mengenai m-LMS, diharapkan dapat mengatasi tantangan yang ada sekarang atau kedepanya dan meningkatkan efektivitas m-LMS untuk menciptakan pengalaman pembelajaran yang lebih fleksibel, responsif, dan inklusif di pendidikan tinggi.
Data Mining Clustering and Correlation Analysis of Marine Potential Insights from Capture Fisheries Coral Reef Quantity and Plankton Abundance Cindy Muhdiantini; Mega Fitri Yani; Ilham Auliya Rahman; Ati Maryati
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 2 No. 1 (2025): January
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.14711890

Abstract

Indonesia, sebagai negara kepulauan dengan wilayah laut yang luas, memiliki potensi besar dalam memanfaatkan sumber daya laut, seperti terumbu karang, mangrove, budidaya, dan penangkapan ikan laut. Pemanfaatan yang berkelanjutan tidak hanya berdampak positif pada ekosistem laut tetapi juga meningkatkan kesejahteraan masyarakat. Untuk mendukung pengelolaan berkelanjutan, diperlukan analisis data mendalam guna mengidentifikasi pola dan hubungan yang relevan. Data mining menjadi alat efektif untuk menggali pola yang tersembunyi, terutama melalui teknik clustering. Analisis clustering dilakukan terhadap data perikanan tangkap, kuantitas terumbu karang, dan kelimpahan plankton guna menemukan kelompok homogen dalam dataset. Proses ini diawali dengan pemilihan data sesuai kriteria, dilanjutkan dengan preprocessing untuk menyaring data redundan. Hasilnya, terdapat tiga cluster utama: cluster 0 berfokus pada terumbu karang, cluster 1 pada jumlah ikan tangkap, dan cluster 2 pada kelimpahan plankton.
Risk Management in Financial Technology: A Systematic Literature Reviewto Support Sustainability and Security of Digital Financial Services Mega Fitri Yani; Cindy Muhdiantini; Syifa Nur Aini
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 2 No. 1 (2025): January
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.14715318

Abstract

The rapid growth of the financial technology (FinTech) sector has revolutionized financial services by enhancing convenience, speed, and efficiency. However, this expansion also introduces significant risks, necessitating robust information technology (IT) risk management strategies. This study systematically reviews the existing literature on FinTech risk management, focusing on frameworks, algorithms, policies, and technical aspects that influence risk management practices. By integrating advanced algorithms such as Artificial Intelligence and Deep Forest with established frameworks like ISO 31000:2018 and NIST, the research provides a comprehensive perspective on managing risks in FinTech, bridging gaps not extensively covered in previous studies. A systematic literature review methodology identified and analyzed 17 key studies from an initial pool of 134 documents sourced from databases such as Scopus and Google Scholar. Findings highlight the critical role of advanced technologies and established frameworks in mitigating risks and underscore the need for continuous adaptation to evolving challenges. This research offers valuable insights for financial institutions and policymakers, promoting sustainable and secure digital financial services.
Pengembangan Aplikasi Laundry Berbasis Android di Wilayah Kota Pekanbaru Muhammad Dwi Hary Sandy
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 2 No. 1 (2025): January
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.14729262

Abstract

Fokus penelitian ini adalah membuat aplikasi penjemputan dan pengantaran cucian bernama Clean Clothes yang dibuat khusus untuk wilayah Kota Pekanbaru. Aplikasi ini berbasis Android dan menggunakan Google Maps API dan Firebase sebagai Backend as a Service (BaaS). Arsitektur layanan mikro digunakan pada aplikasi ini agar dapat berjalan secara mandiri dan memungkinkan pengembangan fitur baru sesuai kebutuhan bisnis dengan cepat tanpa mengorbankan kinerja fungsionalitas aplikasi yang sudah ada. Fokus penelitian ini adalah untuk membantu mengembangkan aplikasi berbasis arsitektur layanan mikro yang dapat digunakan oleh penduduk Kota Pekanbaru untuk penjemputan dan pengantaran cucian yang direalisasikan pada Firebase. Untuk mengembangkan aplikasi ini, model waterfall dengan pendekatan penelitian kualitatif digunakan. Diharapkan aplikasi ini akan meningkatkan kualitas layanan penjemputan dan pengantaran cucian di seluruh Kota Pekanbaru.
Performance Analysis of SVM and Random Forest Algorithms in the Case of the Influence of Music on Mental Health Karisma Septa Kresna; Kusnawi
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 2 No. 2 (2025): April
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.15130408

Abstract

Mental health disorders are conditions that impress a person's behavior, mindset, and emotions. According to WHO data, the rate of mental disorders in Asia has increased significantly in the past two decades, with about one-fifth of the world's adolescent population experiencing stress each year. Music has long been known to have a positive influence on mental health, and music therapy is used as one approach to assist individuals in improving social, mental, and physical conditions. In this study, the authors used data mining techniques to identify relevant patterns regarding the influence of music on mental health. Two classification algorithms, namely the Support Vector Machine (SVM) and Random Forest, is used to analyze and characterize the data. SVM is known to excel at managing high-dimensional data, while Random Forest is effective at handling data with missing outliers and features. This study purpose to oppose the performance of the two algorithms in classifying the influence of music on mental health to identify the superior algorithm in this context. The Random Forest algorithm gets 93% accuracy and SVM gets 95% accuracy, the hyperparameter tuning on the SVM algorithm has a better performance than Random Forest with an accuracy score of 97% for SVM, while for Random Forest it gets an accuracy score of 94%. The results of the study are expected to provide insight into the use of music as a mental health therapy tool.
Performance Analysis of Support Vector Machine and Gradient Boosting Machine Algorithms for Heart Disease Prediction Tegar Wirawan; Kusnawi Kusnawi
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 2 No. 2 (2025): April
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.15126239

Abstract

Cardiovascular disease ranks among the primary causes of mortality globally, with death rates rising each year. Assessing heart disease risk is crucial for enhancing the efficiency of prevention and treatment strategies. This study seeks to evaluate the effectiveness of two machine learning techniques, namely Support Vector Machine and Gradient Boosting Machine, in forecasting heart disease using a dataset obtained from Kaggle. The research process starts with gathering data, followed by exploratory analysis, preprocessing through label encoding, handling class imbalance with SMOTE, and normalizing data using Standard Scaler. Features were selected using the Correlation Thresholding method. Subsequently, the dataset was divided into training and testing sets to develop predictive models. The model performance was assessed using evaluation metrics, including accuracy, precision, recall, and F1-Score. The findings indicate that the Gradient Boosting Machine outperformed the Support Vector Machine, achieving an accuracy of 98% compared to SVM's accuracy of 93%. This research is expected to contribute to healthcare practices by enabling early detection of heart disease risks. Future research is recommended to explore other algorithms or employ more diverse datasets to achieve better results

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