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INDONESIA
SKANIKA: Sistem Komputer dan Teknik Informatika
Published by Universitas Budi Luhur Jakarta
ISSN : -     EISSN : 27214788     DOI : 10.36080
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Engineering
SKANIKA: Sistem Komputer dan Teknik Informatika adalah media publikasi online hasil penelitian yang diterbitkan oleh Program Studi Sistem komputer dan Teknik Informatika, Fakultas Teknologi Informasi, Universitas Budi Luhur. Scope atau Topik Jurnal: Kriptografi, Steganografi, Sistem Pakar / Artificial Intelligence , Sistem Penunjang Keputusan, Bioinformatika, Kecerdasan Komputasional, Semantics Web dan Ontologies, Data Mining,Text Mining,Natural Language Processing, Pengelolaan Citra Digital, Otomasi Berbasis Sensor, Wireless Sensor Network, Network Management dan Maintenance, Sistem Operasi, Sosial Network Analysis, Security, Augmented Reality, Game Development, Virtual Reality, Webservice / API, Internet of Things (IoT)
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 340 Documents
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PENGELOMPOKAN WILAYAH INDONESIA BERDASARKAN KOMPONEN INDEKS PEMBANGUNAN MANUSIA DENGAN PENDEKATAN ALGORITMA K-MEANS CLUSTERING Saputra, Nur Rukhan; Muflih, Ghufron Zaida
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 8 No 1 (2025): Jurnal SKANIKA Januari 2025
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v8i1.3318

Abstract

The Human Development Index (HDI) plays a crucial role in measuring the well-being of a region's population, offering a comprehensive perspective through various indicators, including economic factors such as Gross Domestic Product (GDP). This study focuses on clustering regions in Indonesia based on HDI components using the K-Means Clustering method. The clustering divides the regions into three groups: low, medium, and high clusters, considering dimensions of education, health, and standard of living. The data used includes indicators such as expected years of schooling, mean years of schooling, life expectancy, average monthly income, and per capita expenditure. The Davies-Bouldin Index (DBI) is employed as an evaluation method to measure the quality of cluster separation. The study reveals that K-Means successfully categorizes the regions into three clusters with a DBI value of 1.17, reflecting good cluster separation. This clustering provides valuable insights into the distribution of human development across Indonesia and is expected to assist policymakers in devising effective strategies to improve well-being in each identified cluster.
PENERAPAN MODEL GAMIFIKASI PADA APLIKASI GAME PENDIDIKAN PANCASILA BERBASIS MULTIMEDIA ANIMASI 2D Putri, Risanty Amelia; Wahyu, Sawali
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 8 No 1 (2025): Jurnal SKANIKA Januari 2025
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v8i1.3319

Abstract

Improving the quality of human resources depends on education. However, the low quality of education in Indonesia is often caused by factors such as lack of facilities and infrastructure, high education costs, and unequal educational opportunities. At MI Al-Ihsan, the low learning outcomes of students in Pancasila Education are largely due to the large amount of material and theory that requires students to memorize a lot, then the lack of creativity and innovation in the teaching methods applied by teachers, which tend to be monotonous and uninteresting for students because the facilities and infrastructure are still inadequate. To overcome this problem, technology can be utilized through the application of gamification in learning media. This game is designed to attract students' interest by matching images with time limits, lives, scores, and leaderboards as well as learning materials that are in accordance with the curriculum. The methodology used in developing this application is the Multimedia Development Life Cycle (MDLC) and the application of gamification. The test results using the SUS (System Usability Scale) method obtained a score of 84.7, which means getting an A or Excellent grade. The results of the study showed that learning using game media such as gamification can make learning more interesting and effective. Students become more motivated and interested in studying Pancasila education materials which ultimately improves their learning outcomes.
SISTEM REKOMENDASI KONVERSI PROGRAM MSIB DENGAN MATA KULIAH PRODI INFORMATIKA ITN MALANG MENGGUNAKAN COSINE SIMILARITY Fisabilillah, Difa; Auliasari, Karina; Pranoto, Yosep Agus
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 8 No 1 (2025): Jurnal SKANIKA Januari 2025
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v8i1.3325

Abstract

The Certified Independent Study and Internship Program (MSIB) is part of the Merdeka Belajar Kampus Merdeka (MBKM) initiative, allowing students to convert up to 20 credits from their activities. However, this process is often challenging as universities must match the MSIB syllabus with the curriculum syllabus of the Informatics Engineering program at ITN Malang. Differences in terminology and specific rules between the two syllabi make the process time-consuming and confusing. This study aims to develop a recommendation system to simplify the course conversion process. The system employs a Natural Language Processing (NLP) model based on Transformers to capture textual context and a cosine similarity algorithm to measure the similarity between syllabi. The system evaluation using classification metrics achieved an accuracy of 67%. The precision score reached 71% for the majority class and 50% for the minority class, while recall was 83% and 33%, respectively. The weighted average produced an f1-score of 0.65, indicating satisfactory performance despite class imbalance. These results demonstrate the system's potential to provide reliable recommendations, although further optimization is required to improve performance for the minority class.
ANALISIS SENTIMEN DALAM APLIKASI X TERHADAP PENGUNGSI ROHINGYA DENGAN LSTM Pratama, Andri; Rosyda, Miftahurrahma
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 8 No 1 (2025): Jurnal SKANIKA Januari 2025
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v8i1.3329

Abstract

Rohingya refugees in Indonesia face discrimination and hate speech due to disinformation in Indonesian society The polemic over the influx of Rohingya refugees to Indonesia has created a variety of responses among the public. Understanding the perspectives and reactions of the Indonesian public regarding this issue is crucial, especially in analyzing the growing sentiment. This research was conducted to evaluate the views and sentiments of Indonesians towards the Rohingya through X social media platforms. Data was collected through web scraping using twikit during December 2023, resulting in 17,613 tweets. Labeling was done using the trained IndoBERTweet model with the IndoNLU smsa_doc-sentiment-prosa dataset. The Long Short-Term Memory (LSTM) model is applied with two class balancing methods, Random Oversampling and SMOTE. The results show that with Random Oversampling, the model achieves precision 0.9139, recall 0.9632, F1 score 0.9379, and accuracy 0.9069. Meanwhile, the use of SMOTE resulted in precision 0.9092, recall 0.9445, F1 score 0.9265, and accuracy 0.8906.
ANALISIS SENTIMEN AKHIR MASA JABATAN PRESIDEN JOKOWI PADA MEDIA SOSIAL X MENGGUNAKAN NAÏVE BAYES Salsabilla, Fadiah Nur; Witanti, Arita
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 8 No 1 (2025): Jurnal SKANIKA Januari 2025
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v8i1.3331

Abstract

Penelitian ini bertujuan untuk mengklasifikasikan sentimen terhadap Presiden Joko Widodo di Media Sosial X/Twitter pada masa akhir jabatannya. Metode yang digunakan adalah Complement Naïve Bayes (CNB) dengan penerapan SMOTE (Synthetic Minority Over-sampling Technique) untuk mengatasi masalah ketidakseimbangan data. Evaluasi dilakukan dengan dua variasi rasio data latih dan data uji, yaitu 90:10 dan 80:20. Pada rasio 90:10, model menunjukkan kinerja terbaik dengan mencapai 88% accuracy, precision, recall, dan f1-score. Namun, pada rasio 80:20, kinerja model mengalami penurunan dengan nilai 81% untuk semua metrik. Analisis sentimen menunjukkan bahwa sentimen negatif mendominasi, diikuti dengan sentimen netral dan positif, yang mencerminkan ketidakpuasan publik terhadap kebijakan-kebijakan tertentu pada periode akhir masa jabatan Presiden Jokowi.
OPTIMALISASI FP-GROWTH DENGAN TEKNIK PRUNING UNTUK ANALISIS POLA PEMINJAMAN BUKU UPT PERPUSTAKAAN UNISNU JEPARA Manzis, Akhmad Hossam; Kusumodestoni, R. Hadapiningradja; Mulyo, Harminto
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 8 No 1 (2025): Jurnal SKANIKA Januari 2025
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v8i1.3333

Abstract

In the digital age, libraries continue to adapt to provide access to information and knowledge, support education and research, and preserve cultural heritage. The increasing demand for reading materials along with the development of information technology and digital literacy has led to a surge in the amount of data stored on various information provision platforms including libraries. With the sheer volume of data, the challenges of management, analysis and storage have become increasingly complex. Lack of understanding of readers' preferences, inefficiency in book procurement, and difficulty in determining book layout are problems that arise in library management. Therefore, analyzing circulation data is very important, for example using FP- Growth to find patterns of book borrowing. Items that do not meet the criteria but are still included in the calculation process cause the results to be less significant, but pruning which removes items with low frequency of occurrence can improve the accuracy of the analysis. The results of the FP- Growth calculation reveal a relationship between management and economics books with a support of 27%, Confidence 54%, and Lift 956 which means that the two books have a large influence on each other's occurrence. While pruning the number of rules generated is getting smaller, from 26 to 8, but the rules have a strong relationship.
IMPLEMENTASI ARTIFICIAL INTELLIGENCE DALAM SISTEM PENCARIAN ORANG HILANG DENGAN FACE RECOGNITION STUDI KASUS POLRES KUDUS Nur Hakim, Ahmad Aufan; Murti, Alif Catur; Nindyasari, Ratih
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 8 No 1 (2025): Jurnal SKANIKA Januari 2025
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v8i1.3334

Abstract

This research aims to develop a missing person search system based on facial recognition technology to enhance the effectiveness and efficiency of identification. The system utilizes FaceNet to extract unique facial features from uploaded images, supported by OpenCV (haarcascade_frontalface_default.xml) for initial filtering, ensuring only images with detected faces are processed into the database. For managing large datasets, the HNSW (Hierarchical Navigable Small World) algorithm is implemented for fast indexing, while FAISS (Facebook AI Similarity Search) accelerates feature matching within extensive datasets. The system is designed as a Progressive Web App (PWA) with a user-friendly interface, accessible across various devices. Testing was conducted at the Kudus Police Department, yielding high identification accuracy and significantly faster search times compared to conventional methods. The PWA implementation ensures flexibility and ease of user access. This study concludes that the integration of modern technologies such as FaceNet, HNSW, and FAISS effectively supports missing person searches. These findings contribute significantly to the development of technology-based solutions for handling missing person cases.
IMPLEMENTASI FP-GROWTH DAN FUZZY TSUKAMOTO UNTUK MENENTUKAN PERSENTASE KUOTA JALUR MASUK PERGURUAN TINGGI Khairunnisa, Nafa; Jumadi, Jumadi; Taufik, Ichsan
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 8 No 1 (2025): Jurnal SKANIKA Januari 2025
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v8i1.3337

Abstract

Every university strives to achieve or maintain excellent accreditation. Students who graduate with a satisfactory predicate play an important role in determining the accreditation. According to BAN-PT 2021, a university is considered excellent if it has students with a maximum study period of 4.5 years and an average GPA ≥ 3.25. One way to maintain it is to optimally manage the distribution of quotas for the New Student Admission (PMB) entrance pathway. This study aims to investigate how the variables of GPA, study period, and entry path in the data of graduates relate to each other. To get the association pattern between these variables, FP-Growth is used. Furthermore, the percentage of quota distribution is calculated using Fuzzy Tsukamoto. From this research, the accuracy of the model is 94.42% and the precision value is 62.5%, which indicates that the method used is effective in helping determine the optimal quota distribution for PMB. Thus, these results can be used to support university policies in determining a more appropriate quota distribution to support the achievement of superior accreditation.
IMPLEMENTASI ALGORITMA MULTINOMIAL NAÏVE BAYES UNTUK MENDETEKSI TWEET UJARAN KEBENCIAN BAHASA INDONESIA TERHADAP PSSI Wulan, Riskiana; Hertanto, Indra
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 8 No 1 (2025): Jurnal SKANIKA Januari 2025
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v8i1.3355

Abstract

This study focuses on the application of the Multinomial Naive Bayes algorithm to detect hate speech in Indonesian tweets and test its accuracy level. According to The 2022 World Football Report, around 69% of Indonesia's population shows a high interest in football, creating a positive digital environment. The Dataset used consists of tweet data related to PSSI and politic taken from Twitter, which is then manually labeled into three classes, namely non-HS (Hate Speech), insults and provocations. The Dataset used consists of 2,210 tweets taken from Twitter, then manually labeled into three classes, namely non-HS (Hate Speech), insults, and provocations. Before dividing the Dataset into train and test data, an undersampling technique was applied to handle class imbalance, with the aim of ensuring a balanced distribution between the three categories. After undersampling, the training Dataset consisted of 350 tweets and the test Dataset consisted of 88 tweets. Evaluation of each method was carried out using matrix precision, recall, and F1-score. The results of the study indicate that the Multinomial Naïve Bayes algorithm obtained an accuracy of 62%. This accuracy result is expected to be useful for developing an effective and accurate hate speech detection model on social media platforms, especially Twitter, so that it can help reduce the awareness of the Indonesian people about the dangers of the spread of hate speech.
Sistem Keamanan Rumah Pintar Berbasis Sensor ESP32-Cam dan PIR Dengan Notifikasi Teknologi Bot Whatsapp Hamuda, Hayadi; Setiawan, Anjar
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 8 No 2 (2025): Jurnal SKANIKA Juli 2025
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v8i2.3387

Abstract

A smart home is a system that basically consists of intelligent elements that are interconnected and integrated with each other through the use of internet networks based on the Internet of Things. Today, smart home technology has been utilised in various rooms in contemporary homes. Several components, such as the ESP32-Cam microcontroller, of these smart home devices are installed in the room and include a motion sensor or PIR (Passive Infrared Receiver), buzzer, and WhatsApp notification software. When motion or activity is detected in the room, the components connected and integrated with the internet network will send notifications to a laptop or WhatsApp messaging programme in the form of text and photos. The results of tool testing and overall system testing data show that the PIR sensor can detect motion at a distance of 1 to 3 metres marked by the activation of the buzzer and the appearance of WhatsApp messages with an average delay of 1 to 3 seconds. Experiments were also carried out based on the length of the 5-pin USB cable, and the results showed that the length of the cable affected the delay in sending WhatsApp notifications in addition to wifi or internet connection. WhatsApp notifications take longer to send the longer the cable is. By using this smart home appliance, home dependability and security can be improved. Keywords: ESP32-Cam, PIR Sensor, Smart Home, Whatsapp

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