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INDONESIA
PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer
Published by Universitas Dinamika Bangsa
ISSN : 19076738     EISSN : 25280082     DOI : -
Core Subject : Science,
Computer Science & IT
Jurnal PROCESSOR merupakan Jurnal yang diterbitkan oleh STIKOM DINAMIKA BANGSA JAMBI. Jurnal ini terbit 2 kali dalam setahun yaitu pada bulan April dan Oktober. Misi dari Jurnal PROCESSOR adalah untuk menyebarluaskan, mengembangkan dan menfasilitasi hasil penelitian mengenai Ilmu bidang informatika, sebagai media bagi para dosen, guru, peneliti dan para praktisi dalam bidang teknologi informasi dari seluruh Indonesia, dalam melakukan pertukaran informasi tentang hasil-hasil penelitian terbaru yang telah dilakukan.
Arjuna Subject : -
Articles 483 Documents
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Analisis Performa Orbital Angular Momentum-Index Modulation Komunikasi Jarak Dekat 6G Waveform Sutanto, Yudi Sutanto; Teguh Yoga Putra
Jurnal PROCESSOR Vol 19 No 1 (2024): Jurnal Processor
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/processor.2024.19.1.1656

Abstract

Currently, wireless communication is migrating from 5G to 6G. This is happening due to the new application of Sensory Transmission technology, Intelligent Transportation System (ITS), and Digital Twin. The problems that arise from the implementation of this migration are the band limits referred to in the regulations above, the reliable link range or the distance that communication is successful, and the increase in the number of users to be served. This study uses the performance results of Orbital Angular Momentum-Index Modulation (OAM-IM) compared to Orthogonal Frequency Division (OFDM) for short distance communication for each Bit Error Rate (BER) at the distances mentioned in the general description. The OAM-IM method can produce lower BER values than the OFDM method through descriptive statistical analysis, The simulation results are described in the form of tables and charts and compared to the lower BER of the two methods. The average BER of OFDM is 0.486818 while a lower BER is recorded in the OAM-IM method of 0.314013 for the proposed simulation.
Analisis Sentimen Mahasiswa Terhadap Kurikulum Literasi Digital di Universitas Singaperbangsa Karawang Menggunakan Naïve Bayes Luthfiyyah, Ibtihal Qomariyyah; Sari, Betha Nurina; Ridwan, Taufik
Jurnal PROCESSOR Vol 19 No 1 (2024): Jurnal Processor
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/processor.2024.19.1.1669

Abstract

Implementation of the Digital Literacy Curriculum at University of Singaperbangsa Karawang (Unsika) as a concrete step considering the importance of digital literacy. Curriculum evaluation is an important step in ensuring the effectiveness of learning. Currently there is no mechanism from Unsika to encourage students to provide feedback on curriculum implementation. One way is by sentiment analysis, which requires sentiment analysis using the Naïve Bayes algorithm which contributes to providing feedback on curriculum evaluation. This sentiment analysis process uses the CRISP-DM (Cross Industry Standard Process for Data Mining) methodology. In an effort to obtain student perceptions, a survey was conducted using a questionnaire. The minimum number of respondents determined using the Slovin formula is 388. In this study the amount of data used was 591. There is an imbalance in the data, to overcome this an oversampling technique can be used using ADASYN (Adaptive Synthetic Sampling Approach). The data has been cleaned to produce 347 positive sentiments and 176 negative sentiments. The results of this research show the best model and word frequency in the form of visualization of words that play an important role in each sentiment category for use in curriculum evaluation. Of the eight model scenarios tested, the model trained with the Naïve Bayes algorithm using a division of 90% training data and 10% testing data with the application of ADASYN became the best model with an accuracy of 89%, precision 100%, recall 85%, and f1-score 92%.
Klasifikasi Penyakit Monkeypox dengan Menggunakan Algoritma K-Nearest Neighbor Suyanti; Arvita, Yulia; Siswanto, Agus
Jurnal PROCESSOR Vol 19 No 2 (2024): Jurnal Processor
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/processor.2024.19.2.1616

Abstract

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%.
Uji Keamanan Back End Aplikasi Berbasis Website Menggunakan Metode Black Box Testing Abd. Wahab Syahroni; Nindian Puspa Dewi; Nilam Ramadhani; Ubaidi; Badar Said
Jurnal PROCESSOR Vol 19 No 2 (2024): Jurnal Processor
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/processor.2024.19.2.1752

Abstract

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.
Fuzzy Time Series Model Lee Dalam Memprediksi Nilai Ekspor di Indonesia Nanda, Yulanda Rahmadiyah; Syamsyida Rozi; Corry Sormin; Yuliana Safitri
Jurnal PROCESSOR Vol 19 No 2 (2024): Jurnal Processor
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/processor.2024.19.2.1794

Abstract

− 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.
Integrasi Metode Viola Jones dan Algoritma Pelabelan Untuk Akurasi Deteksi Objek Manusia Ardi Wijaya; Yudha, Bima Satria; Yovi Apridiansyah; Nuri David Maria Veronika
Jurnal PROCESSOR Vol 19 No 2 (2024): Jurnal Processor
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/processor.2024.19.2.1822

Abstract

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%.
Sistem Pemantauan Infus Berbasis IoT dengan Notifikasi Real-Time Melalui Telegram Teodora Fenny Aliansih; Rahmi Hidayati; Kartika Sari
Jurnal PROCESSOR Vol 19 No 2 (2024): Jurnal Processor
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/processor.2024.19.2.1843

Abstract

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.
Sistem Pendukung Keputusan Dalam Menentukan Siswa Berprestasi Menggunakan Metode Simple Additive Weighting (Studi Kasus: MA Sahid) Nurul Kamilah; Adhitya Nurachman, Muhammad; Dewi Primasari
Jurnal PROCESSOR Vol 19 No 2 (2024): Jurnal Processor
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/processor.2024.19.2.1864

Abstract

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.
Pengembangan Representasi Pengetahuan Ontologi Domain Bidang Ilmu Informatika Rodiah, Desty; Kanda Januar Miraswan; Junia Kurniati; Dellin Irawan; Vanya Terra Ardiani
Jurnal PROCESSOR Vol 19 No 2 (2024): Jurnal Processor
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/processor.2024.19.2.1905

Abstract

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.
Analisis Perbandingan QoS Live Streaming Facebook dan Instagram di Kawasan Pariwisata Night Market Labuan Bajo Jamal, Venny Aprilia; Ariessaputra, Suthami; Muvianto, Cahyo Mustiko Okta Muvianto
Jurnal PROCESSOR Vol 19 No 2 (2024): Jurnal Processor
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/processor.2024.19.2.1913

Abstract

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.

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