Jurnal PROCESSOR

Sistem Pendukung Keputusan dalam Menentukan Siswa Berprestasi Menggunakan Metode Simple Additive Weighting

Nurul Kamilah, Muhammad Adhitya Nurachman, Dewi Primasari — Thu, 31 Oct 2024 00:00:00 +0800

Madrasah Aliyah Sahid always holds a selection of outstanding students every semester. The selection of exceptional students is carried out per class by determining the highest score from rank 1 to 3. However, the assessment of outstanding students is still based only on report card scores, so other aspects besides report card scores are not considered as criteria for exceptional students. This raises various problems such as students who have good report card scores but have shortcomings in several other aspects, which means that the selected outstanding students do not reach the required standards and do not get the best candidates. In dealing with this problem, a decision support system is needed to determine outstanding students using the SAW method. This method can produce the best data because it is done by finding the criteria and weight values of each attribute, which is expected to help the school in carrying out the process of assessing outstanding students so that the results obtained are more effective and efficient. These criteria include violation points, achievement points, attendance, and attitudes and behavior. After the criteria are determined, a decision support system is created to determine outstanding students. The system development uses Waterfall which consists of needs analysis, system design, implementation, testing, and improvement process. The results of the system design are in the form of criteria and weight calculations to produce a decision matrix, a normalized matrix, and the results of the calculations in the SAW method.

Integration of Viola Jones Method and Labeling Algorithm for Human Object Detection Accuracy

Ardi Wijaya, Bima Satria Yudha, Yovi Apridiansyah, Nuri David Maria Veronika — Thu, 31 Oct 2024 00:00:00 +0800

Motion detection is a key element in modern surveillance systems to ensure optimal security. However, the accuracy of motion detection is often a challenge under various environmental conditions. This research investigates the use of the Viola-Jones method and labeling algorithm as a solution to improve object detection accuracy by sampling 15 videos that record various environmental conditions, including indoors, outdoors, and at night. The Viola-Jones method is implemented for face detection as a first step in human object identification, while the labeling algorithm is used to refine and validate the detection results in more detail. Experimental results show that combining the two methods succeeded in increasing object detection accuracy. Of the 15 videos analyzed, only 4 videos experienced inaccurate detection results, while the other 11 videos managed to get accurate results. Evaluation of system performance using Precision, Recall, and Accuracy metrics produces a Precision rate of 73.33%, a Recall rate of 100%, and an Accuracy rate of 73.33%. Apart from that, manual calculations were also carried out which resulted in an accuracy rate of 91.77%.

Security Testing of Web-Based Application Back End Using Black Box Testing Method

Abd. Wahab Syahroni, Nindian Puspa Dewi, Nilam Ramadhani, Ubaidi, Badar Said — Thu, 31 Oct 2024 00:00:00 +0800

Application security is often overlooked during the development phase and even after the application is deployed. However, without proper security measures, even the most advanced technologies can lead to significant losses, such as unauthorized data access and potential data loss due to deletion actions. Developing applications using the REST API architecture allows users to access backend endpoints from outside the application, so attention must be given not only to authentication but also to authorization issues. Based on the results of testing the SILAB application using the Black Box Testing method, it can be concluded that the SILAB application still needs improvements in backend security, particularly in terms of authorization. This indicates that there are still vulnerabilities and threats that could potentially compromise the data in the SILAB application.

Penerapan Algoritma Fuzzy Mamdani Pada Monitoring dan Sistem Kontrol Pemakaian Kipas Angin di Ruangan Berbasis Internet Of Things

Novita Sari Novi, Denok Wulandari Denok, Sahrul — Thu, 31 Oct 2024 00:00:00 +0800

Kipas angin memiliki peranan penting dalam menciptakan lingkungan yang nyaman dan efisien secara energi. Kipas angin digunakan secara luas dalam berbagai ruangan untuk mengatur suhu udara dan meningkatkan kenyamanan pengguna. kondisi lingkungan yang berubah-ubah seperti suhu udara, kelembaban, dan tingkat kehadiran pengguna dapat mempengaruhi kebutuhan pengoperasian kipas angin. Namun, pemakaian kipas angin yang tidak terkontrol secara efisien seperti kipas angin yang selalu menyala meskipun tidak ada orang didalam ruangan, jumlah kipas angin yang banyak menyala meskipun orangnya sedikit dan masalah lainnya dapat mengakibatkan pemborosan pemakaian energi Listrik. Berdasarkan permasalahan diatas, penulis bermaksud melakukan penulisan dengan menerapkan algoritma fuzzy mamdani pada alat monitoring dan kontrol pemakaian kipas angin pada ruangan berbasis Internet Of Things dengan mengkombinasikan sensor optik, Sensor Suhu DHT 22, Wemos D1, Motor DC, dan LCD 16 x 2 dan diprogram menggunakan Bahasa Pemrograman C. Adapun prinsip kerja alat ini yaitu jumlah kipas angin yang akan nyala dilihat berdasarkan jumlah orang, suhu, dan kelembapan pada ruangan. Selain itu, alat ini juga dapat dimonitoring dan dikontrol menggunakan Smartphone Android melalui jaringan internet. Sehingga diharapkan dengan penulisan ini bertujuan untuk mengefesiensikan pemakaian kipas angin yang dapat menghemat pemakaian energi listrik

Development of Ontology Knowledge Representation in Computer Science

Thu, 31 Oct 2024 00:00:00 +0800

Research in computer science, which often involves complex issues, frequently encompasses multiple sub-disciplines. The more research that applies multiple sub-disciplines, it becomes challanging to categorize the appropriate branches of knowledge related to the research. Therefore, a knowledge representation is needed to accurately depict these fields of study. This research develops an ontology that serves as a knowledge representation for computer science, comprising four sub-disciplines: graphics and visualization, natural language processing, distributed systems, and data science and pattern recognition.The ontology development is based on the grouping references from the Association for Computing Machinery (ACM). Using the Protégé software version 5.5.0, the development resulted in a matrix with 3,584 axioms, 837 logical axioms, 794 classes, and 1 equivalent class. Once the ontology was successfully developed, it underwent testing through query examinations, with four specific queries for each sub-discipline. The query testing utilized a filter based on keywords input by the user. The keywords used were graphics, words, security, and patterns. The ontology successfully provided answers based on the exploration of relationships between subclasses within the ontology.

IoT-Based Infusion Monitoring System with Real-Time Notifications via Telegram

Teodora Fenny Aliansih, Rahmi Hidayati, Kartika Sari — Thu, 31 Oct 2024 00:00:00 +0800

Infusions are clinical equipment for administering liquid medication or nutrients to patients. Infusion fluid is stored in a sterile bag and delivered to a vein through a tube. Infusion monitoring is usually done manually by nurses, which often results in negligence such as replacing the liquid run out too late, handling infusions that are not dripping, and dealing with blood that rises into the infusion hose. Therefore, a real-time monitoring system is needed that can provide information and remote notifications about the condition of the infusion. This research develops an IoT-based system using NodeMCU ESP32, Loadcell sensor, TCRT5000 sensor, LDR sensor, Node-RED, and Telegram bot application. NodeMCU ESP32 serves as the main controller. Loadcell sensor gauges volume of the infusion fluid, TCRT5000 sensor counts droplets per minute, and LDR sensor detects blood in the infusion hose. Sensor data is sent to Node-RED for processing and display on the Telegram bot application. The accuracy of the Loadcell sensor reaches 95.69%, while the TCRT5000 sensor has an accuracy of 76.73%. Notifications are sent when the infusion fluid volume is less than 100 ml, the infusion fluid is not dripping, and when the LDR sensor detects blood in the infusion hose.

Fuzzy Time Series Model Lee dalam Memprediksi Nilai Ekspor di Indonesia

Yulanda Rahmadiyah Nanda, Syamsyida Rozi, Corry Sormin, Yuliana Safitri — Thu, 31 Oct 2024 00:00:00 +0800

− Exports are an important indicator in the Indonesian economy. In April 2022, the Central Bureau of Statistics (BPS) reported that Indonesia's export value reached USD 27.32 billion, an increase of 47.76% compared to the previous month. Unstable export values are influenced by exchange rate fluctuations, changes in market demand, and other external factors, making business planning and trade between countries difficult. This study aims to predict the value of Indonesian exports using Lee's Fuzzy Time Series method and measure its accuracy with Mean Absolute Percentage Error (MAPE). The data used is secondary data from BPS, covering export values from January 2021 to February 2024. The steps in this method include determining the universe set, interval formation, fuzzy set, fuzzification of data, determination of Fuzzy Logic Relationship (FLR), formation of Fuzzy Logic Relationship Group (FLRG), defuzzification, and prediction results. the results showed that prediction with Fuzzy Time Series Lee has an error rate of 5.1568% (MAPE), which is in the excellent category. The predicted value of Indonesia's exports for March 2024 is US$ 21,850.69 million.

Simple Additive Weighting Method to Determine the Number of TB Drug Stocks at Bagan Asahan Health Center

Dhea Sela, Abdul Halim Hasugian — Thu, 31 Oct 2024 00:00:00 +0800

The availability of adequate drugs is very important in maintaining the quality of health services at the Bagan Asahan Health Center, especially for the treatment of TB disease. One of the main challenges faced is determining the right amount of TB drug stock to prevent shortages or excess stock, which can affect the effectiveness of services to patients. To overcome this problem, this study uses the Simple Additive Weighting (SAW) method implemented in a web-based decision support system. This system is designed to assist in determining the amount of drug stock needed accurately based on data collected from the Bagan Asahan Health Center. The data analyzed includes drug demand in the previous month and the final amount of drug stock. Through the SAW method, the data is processed to produce a drug ranking and prioritize the required stock replenishment. This study aims to provide a system that is not only efficient and structured, but also helps health workers in managing drug supplies optimally, thereby increasing accuracy in stock replenishment and efficiency in the drug procurement process. The results of testing through simulation and real data analysis show that this system is able to provide more accurate recommendations for drug stock management. In addition, this system is able to produce periodic stock reports, which can be used by health center management to make drug procurement decisions appropriately and efficiently.

Klasifikasi Penyakit Monkeypox dengan Menggunakan Algoritma K-Nearest Neighbor

Suyanti, Yulia Arvita, Agus Siswanto — Thu, 31 Oct 2024 00:00:00 +0800

Dengan berkembangnya penyakit mokeypox maka banyak masyarakat bisa tertular penyakit ini, di karenakan peyakit ini penularannya bisa melalui kontak erat dengan hewan/orang yang terinfeksi monkeypox. Penyebaranya bisa melalui kontak tatap muka, kulit ke kulit, mulut ke mulut, atau mulut ke kulit, termaksud kontak seksual, percikan ludah/cairan hidung, dan mungkin penularan bisa melalui aerosol jarak pendek. jika virus ini tertular ke bayi yang baru lahir, anak – anak, dan orang dengan gangguan kekebalan tubuh maka dapat beresiko mengalami gejala – gejala yang lebih serius dan menyebabkan kematian. Untuk mengurangin penyebaran penyakit ini dapata dilakukan diagnosa terhadap faktor – faktor terkait, akan tetapi terdapat resiko jika salah dalam mengklasifikasikan. Untuk mengatasi hal tersebut maka dapat memanfaatkan metode dalam data mining. Untuk menghentikan penyebaran virus monkey pox secara luas maka dapat di lakukan dengan menerapkan model pembelajaran data mining yaitu dengan cara mengklasifikasikan penyakit monkeypox dengan menggunakan metode K-Nearest Neighbor Sehingga hal ini bisa menghentikan penyebaraan virus semakin banyak dan meluas serta bisa mendeteksi dini penyakit monkeypox dan menghambat penularan serta pertumbuhan kematian yang di akibatkan oleh virus. Hasil implementasi algortima KNN pada aplikasi ripedminer dilakukan dengan menggunakan pergantian nilai k, dan hasil akurasi tertinggi didapat pada nilai k=9 dengan akurasi sebesar 59,50%, nilai presesinya adalah 65,93 %, sedangkan recall menghasilkan 76,92%.

Analisis Perbandingan QoS Live Streaming Facebook dan Instagram di Kawasan Pariwisata Night Market Labuan Bajo

Venny Aprilia Jamal, Suthami Ariessaputra, Cahyo Mustiko Okta Muvianto Muvianto — Thu, 31 Oct 2024 00:00:00 +0800

Kemajuan teknologi informasi telah mempermudah akses dan distribusi informasi melalui berbagai platform media sosial seperti Facebook, Instagram, X, dan WhatsApp. Salah satu penggunaan yang populer adalah live streaming, yang digunakan untuk berbagi kegiatan secara real-time. Penelitian ini dilakukan d kawasan Night Market Labuan Bajo dengan bertujuan untuk menganalisis dan membandingkan QoS live streaming aplikasi Facebook dan Instagram di kawasan night market Labuan Bajo. Penelitian ini dilakukan selama 30 hari dan hasil penelitian menunjukkan bahwa nilai throughput untuk Facebook dan Instagram berada pada indeks 2 (kategori sedang) untuk tiga resolusi (480p 30fps, 720p 30fps, dan 1080p 30fps), sementara nilai packet loss, delay, dan jitter berada pada indeks 4 (kategori sangat bagus). Nilai throughput Facebook lebih baik dibandingkan Instagram, terutama pada resolusi 480p 30fps dengan nilai 2024,809 kbps. Nilai packet loss terbaik untuk sore hari ditemukan pada Instagram dengan resolusi 1080p 30fps sebesar 1,98 ms. Delay terbaik tercatat pada Instagram dengan resolusi 1080p 30fps, baik di pagi maupun sore hari. Jitter terbaik di pagi hari dicapai oleh Facebook dengan resolusi 1080p 30fps, sementara sore hari lebih unggul pada Instagram dengan resolusi yang sama.