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Tri A. Sundara
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The Indonesian Journal of Computer Science
Published by STMIK Indonesia Padang
ISSN : 25497286     EISSN : 25497286     DOI : https://doi.org/10.33022
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering Engineering
The Indonesian Journal of Computer Science (IJCS) is a bimonthly peer-reviewed journal published by AI Society and STMIK Indonesia. IJCS editions will be published at the end of February, April, June, August, October and December. The scope of IJCS includes general computer science, information system, information technology, artificial intelligence, big data, industrial revolution 4.0, and general engineering. The articles will be published in English and Bahasa Indonesia.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 39 Documents
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Search results for , issue "Vol. 12 No. 4 (2023): The Indonesian Journal of Computer Science (IJCS)" : 39 Documents clear
Performance Analysis of CT-Scan Covid-19 Classification Using VGG16-SVM Buana, Rifqi Genta; Abdulloh, Ferian Fauzi
The Indonesian Journal of Computer Science Vol. 12 No. 4 (2023): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i4.3275

Abstract

The world was shaken by the emergence of a deadly virus variant called Severe Acute Respiratory Distress Syndrome CoronaVirus 2 which causes COVID-19 disease. This phenomenon started at the end of 2019 which later became an outbreak that caused a deadly pandemic. A significant number of people lose their lives because of this outbreak. A fast and precise diagnosis is needed so that the patients can be treated immediately. This study is intended to overcome these problems by utilizing machine learning to classify lung CT-Scan images. This study propose to use the Convolutional Neural Network (CNN) based on Visual Geometry Group (VGG) 16 layers architecture and Support Vector Machine (SVM) as its classifier. The classification results of the proposed method achieve 89% and 96% accuracy on the two different datasets. This study results can help overcome problems related to the COVID-19 diagnosis and the lack of resources to classify images. The world was shaken by the emergence of a deadly virus variant called Severe Acute Respiratory Distress Syndrome CoronaVirus 2 which causes COVID-19 disease. This phenomenon started at the end of 2019 which later became an outbreak that caused a deadly pandemic. A significant number of people lose their lives because of this outbreak. A fast and precise diagnosis is needed so that the patients can be treated immediately. This study is intended to overcome these problems by utilizing machine learning to classify lung CT-Scan images. This study propose to use the Convolutional Neural Network (CNN) based on Visual Geometry Group (VGG) 16 layers architecture and Support Vector Machine (SVM) as its classifier. The classification results of the proposed method achieve 89% and 96% accuracy on the two different datasets. This study results can help overcome problems related to the COVID-19 diagnosis and the lack of resources to classify images.
Child drowning prevention: GPS and LoRa based emergency alert system Enam, Md. Rayef; Ghosh, Subhasish; Sarker, Dr. M. Mesbahuddin
The Indonesian Journal of Computer Science Vol. 12 No. 4 (2023): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i4.3277

Abstract

UNICEF recently published in the "Country Office Report 2021" about the mortality of children in Bangladesh, "Every day, 30 children die from drowning – Bangladesh's second leading cause of under-five mortality. Drowning is preventable, and most cases occur within a child's home community." Bangladesh is a country of rivers, which means Bangladesh called a riverine country located in South Asia. The Center for Injury Prevention and Research conducted a survey, Around 19000 people of (all types of ages) drown every year in Bangladesh. Among them, 14500 which mean 77% are children. In our research, the emergency alert system is designed to be cost-effective and user-friendly for village communities in Bangladesh. The system is divided into two components: the kid is equipped with the transmitter, and the receiver is placed at home. The transmitter and receiver both use a LoRa transmission module that can communicate accurately within a 300-meter range (coverage up to 10 Kilometers) and can transmit 256 bytes of data. The transmitter collects geolocation data using a GPS module and sends the data to the receiver using the LoRa module. The receiver module is configured by setting up the geolocation of risky places. The receiver will send SMS or buzzing the receiver to alert the parents when the transmitter or kid is nearby risky places. The Equirectangular approximation method calculates the distance between children's positions from risky areas. Additionally, the transmitter and receiver may communicate encrypted messages using AES 128-bit symmetric encryption technology compatible with Arduino Nano controller. Thus, our emergency alert system can save children from drowning in the home environment.
The Role of Artificial Intelligence and Machine Learning in Smart and Precision Agriculture Bezas, Konstantinos; Foteini Filippidou
The Indonesian Journal of Computer Science Vol. 12 No. 4 (2023): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i4.3278

Abstract

In recent years, the agricultural sector has been undergoing a new "green revolution" characterized by the increasing use of information and communication technology (ICT) and the transition from traditional farming methods to smart agricultural practices, also known as Agriculture 4.0. Robotics, combined with the use of drones, the emerging field of the Internet of Things (IoT), machine learning, and artificial intelligence, are now being deployed in digital transformation services in agriculture, aiming to optimize crop performance and agricultural sustainability. According to research and international literature, the new trends are now oriented towards the development of global and state-of-the-art connected agricultural systems through digital management platforms, with the goal of facilitating the flow of data and information. Although many efforts are being made to implement smart agriculture, there are still challenges that require further research.
Analisa Perbandingan Algoritma CNN dan LSTM untuk Klasifikasi Pesan Cyberbullying pada Twitter Radjavani, Alifqi; Bayu Sasongko, Theopilus
The Indonesian Journal of Computer Science Vol. 12 No. 4 (2023): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i4.3287

Abstract

Dengan meningkatnya penggunaan sosial media, cyberbullying telahmencapai titik puncak sepanjang masa. Anonimitas pada internet membuatcyberbullying sangat merusak, dikarenakan korban akan merasa jika tiadajalan keluar dari pelecehan tersebut. Setiap individu harus selalu waspadaterhadap cyberbullying dan dihimbau untuk selalu melindungi diri sendiribeserta orang lain dari hal ini. Pada kasus ini, penulis membuat model yangsecara otomatis akan menandai tweet yang berpotensi membahayakan sertamemecah pola pesan kebencian tersebut. Dataset yang disediakan olehpenulis berisi sekitar 48.000 tweet yang telah dilabeli sesuai dengan jenis dandata-data tersebut telah diseimbangkan dan berisi sekitar 8000 data.Penelitian ini membandingkan algoritma Convolutional Neural Networkdengan Long Short-Term Memory untuk menentukan algoritma terbaik untukdataset pada penelitian ini. Berdasarkan hasil penelitian yang sudahdilakukan disimpulkan jika Long Short-Term Memory adalah algoritmaterbaik dengan f1-score 83.09%.
Usability Evaluation Of Social Security For Workers Applications In Public Institutions Afrizon, Asra; Hadi Putra, Panca
The Indonesian Journal of Computer Science Vol. 12 No. 4 (2023): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i4.3288

Abstract

Workforce social security application services are owned by the Indonesian government and held by the Social Security Administering Body on Employment. Initially, the application only provided social security programs, membership, and benefits information. In September 2021, the application was transformed by including transactions for claim redemption. User reviews of the application gave ratings of 1.5 and 4.4, respectively meanwhile complaints and requests for information in 2022 were the second highest, accounting for 65% and 5%, respectively. Through analysis of user reviews and complaints, it has been determined that users encounter multiple constraints when utilizing applications concerning accessibility, information, authentication, procedures, and comprehension of usage. The research method involves a quantitative approach using a user experience questionnaire and usability testing methods through task scenarios. Followed by a qualitative approach using a post-test interview to conduct deep dive into user problems and expectations. This study aims to evaluate the application's usability and provide recommendations for improvement. Based on the analysis from UEQ, the application get an average value positive, and the benchmark result is above average. In contrast, the novelty gets neutral, and the benchmark result is below average, reviewed as conventional, slow, less valuable, challenging to learn, and monotonous. The test task scenario described effectivity of application. Meanwhile, time-based efficiency is 0,04 goals/second, which can be interpreted as high speed. Task scenario finding explains 30 problems in which three tasks are prioritized. Post-test interviews explained user need for frequently accessed features, difficulty to use, and usage limitations. The recommendations aimed to adjust the interface and functionality to meet user needs, providing a satisfying experience and shortening the self-service process.
Analisis Kinematika Maju dari Tangan Robotik Berjari 4 yang Digunakan pada Robot Humanoid T-FLoW Apriandy, Kevin; Dewantara, Bima Sena Bayu; Dewanto, Raden Sanggar; Pramadihanto, Dadet
The Indonesian Journal of Computer Science Vol. 12 No. 4 (2023): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i4.3291

Abstract

Model kinematika merupakan bagian penting dalam pengembangan robot humanoid karena dapat merepresentasikan karakteristik dari robot, membuat pemahaman tentang robot menjadi lebih mudah. Mengingat perkembangan robot humanoid T-FLoW yang saat ini dilengkapi dengan sepasang tangan baru, maka perlu dibangun model kinematika untuk memahami lebih lanjut tentang tangan robot baru tersebut. Oleh karena itu, dalam pekerjaan ini, disajikan sebuah analisis kinematika maju untuk memperoleh model kinematika dari tangan berjari 4 baru robot humanoid T-FLoW. Dengan menggunakan pendekatan matriks transformasi homogen, model kinematika tangan robot diturunkan berdasarkan perkalian beberapa matriks rotasi dan matriks translasi yang tersusun dari frame koordinat pangkal ke frame koordinat tujuan. Model kinematika yang diturunkan disimulasikan dalam tugas gerak dasar tangan: menggenggam sebuah benda, dihitung dengan bantuan MATLAB, dan divisualisasikan menggunakan fitur plot 3D MATLAB. Hasil menunjukkan bahwa model tersebut memberikan berbagai karakteristik tangan robot seperti konfigurasi, posisi sendi, dan posisi end-of-effector, yang kemudian dapat divisualisasikan menjadi kerangka tangan. Kedepannya, pekerjaan kami dapat memfasilitasi pengembang T-FLoW dalam membangun pergerakan tangan dengan sistem umpan balik, yang kemudian dapat digunakan untuk menyelesaikan berbagai permasalahan desain gerakan tangan. Kinematics models are important part of humanoid robot development as they can represent the characteristics of the robot, making understanding the robot easier. Given the development of the T-FLoW humanoid robot which is currently equipped with a new pair of hands, it is necessary to build a kinematics model to understand more about the new robot hands. Therefore, in this work, a forward kinematics analysis is presented to derive the kinematics model of the new 4-fingered T-FLoW humanoid robot hand. Using a homogeneous transformation matrix approach, the kinematics model of the robot hand is derived based on the multiplication of several rotation and translation matrices arranged from the base coordinate frame to the goal coordinate frame. The derived kinematics model is simulated in a basic hand motion task: grasping an object, calculated with the help of MATLAB, and visualized using MATLAB's 3D plot feature. The results show that the model provide various characteristics of the robot hand such as configuration, joint positions, and end-of-effector positions, which then be visualized into a hand skeleton. In the future, our work can facilitate T-FLoW developers in building hand movement and feedback systems, which then can be used to solve various hand motion design problems.
Pengaruh Penerapan Aplikasi Google Classroom Terhadap Motivasi Belajar, Minat Belajar dan Hasil Belajar Peserta Didik Pada Mata Pelajaran Prakarya dan Kewirausahaan Ponimin, Ponimin; Mukhaiyar, Riki; Hendriyani, Yeka; Maksum, Hasan
The Indonesian Journal of Computer Science Vol. 12 No. 4 (2023): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i4.3293

Abstract

Kemajuan teknologi di era modern ini memberikan kesempatan kepada guru untuk mengembangkan kemampuannya dalam penggunaan media dan bahan ajar. Mengubah paradigma yang digunakan oleh instruktur dalam melaksanakan proses pembelajaran di sekolah sangat penting untuk menyelaraskan proses modernisasi dan kualitas pembelajaran. Saat ini, guru juga harus mahir dalam menggunakan dan mengoperasikan teknologi informasi sehingga mereka dapat menggunakannya untuk meningkatkan pembelajaran di kelas. Pengetahuan kewirausahaan ditemukan dalam mata pelajaran seperti kerajinan dan kewirausahaan, antusiasme yang besar untuk memahaminya. Oleh karena itu, media pembelajaran perlu didukung dengan media yang dapat digunakan dalam pembelajaran kerajinan dan kewirausahaan. Penelitian dilakukan sebagai upaya untuk mengetahui pengaruh Google Classroom terhadap motivasi belajar, minat belajar dan hasil belajar peserta didik, kemudian Penelitian dilaksanakan di SMK Negeri 1 Tualang Kecamatan Tualang Kabupaten Siak dengan subjek penelitian siswa kelas XI yang berjumlah 95 peserta didik. Data dikatakan valid dan reliabel pada pengaruh Google Classroom terhadap motivasi belajar, minat belajar dan hasil belajar peserta didik. Sedangkan hasil penelitian melalui kuesioner dengan jumlah responden sebanyak 33 orang diperoleh data variabel Google Classroom dengan skor sebesar 40. Sedangkan hasil perhitungan Mean yaitu 33,18, Median adalah 33, dan Modus berjumlah 29. Dari hasil tersebut maka dapat disimpulkan bahwa kecenderungan penggunaan Google Classroom berbanding lurus dengan skor yang diperoleh. diketahui bahwa tanggapan responden tentang Minat belajar sebelum pembelajaran, dimana dari 33 responden yaitu 21 responden atau sebanyak 63,6% responden menyatakan sangat setuju, 12 responden atau sebanyak 36,4%.Hal ini ditunjukkan menggunakan regresi linear berganda diperoleh nilai r 0.847, Nilai Adjusted R2 sebesar 0.688, dan nilai thitung ttabel (2,357 2,045) dengan signifikansi 0.025 (pengujian dua sisi), Penggunaan Google Classroom berpengaruh positif terhadap hasil belajar peserta didik pada mata pelajaran prakarya dan kewirausahaan menggunakan regresi logistik ordinal diperoleh nilai R2 (Nagelkerke) sebesar 0.746, dan nilai estimate sebesar 0.892 yang dieksponensialkan menjadi 2.44 dengan signifikansi 0.016 0.05. Pada penggunaan Google Classroom maka akan semakin baik motivasi belajar dan minat pula hasil belajar peserta didik pada mata pelajaran prakarya dan kewirausahaan kelas XI di SMK Negeri 1 Tualang Kabupaten Siak.
Perbandingan K-Nearest Neighbors, Support Vector Dan Random Forest Pada Prediksi Medical Cost Anggista Oktavia Praneswara
The Indonesian Journal of Computer Science Vol. 12 No. 4 (2023): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i4.3298

Abstract

Asuransi kesehatan adalah kontrak yang mengharuskan membayar sebagian atau seluruh biaya perawatan terkait masalah kesehatan yang dialami. Pengguna asuransi harus membayar premi dengan membayar iuran dalam periode yang telah ditentukan. Dalam praktiknya, pembayaran premi asuransi kesehatan bisa langsung dipotong dari gaji bulanan yang didapat. Maka dari itu, penelitian ini dilakukan dengan mengimplementasikan sebuah algoritma prediksi biaya medis yang dikeluarkan per individu dengan menggunakan perbandingan 3 algoritma yaitu K-Nearest Neighbor, Support Vector Machine dan Random Forest dengan dataset yang diambil kaggle dengan nama insurance.csv berdasarkan kolom usia, jenis kelamin, indeks Massa Tubuh ( BMI ), jumlah anak dalam satu keluarga, individu perokok atau tidak, wilayah tempat tinggal penerima asuransi kesehatan dan biaya medis yang ditanggung oleh asuransi kesehatan. Metode penelitian dilakukan dengan pemeriksaan data dengan melakukan analisi pada dataset serta membagi data menjadi data training dan data test. Hasil penelitian pada algoritma KNN memiliki nilai prediksi MSE sebesar 9651.5, algoritma Random Forest memiliki nilai prediksi MSE sebesar 9755.4, sedangkan algoritma SVM memiliki nilai prediksi MSE sebesar 9312.6.
Fake News Detection Using Machine Learning Mohammed Amin, Pshko
The Indonesian Journal of Computer Science Vol. 12 No. 4 (2023): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i4.3310

Abstract

Today one of the fattest environments for opinion exchanging is the internet. Individuals can share their data or post any news they want through social media platforms. these data sharing platforms do not verify the users or the data and information they post. so, some users can share fake or untrusted data through these platforms on the Internet. Fake news is described as a propaganda tool against any Individual, society, government, or political party. Humans cannot detect and understand all the fake news or data through these online platforms. In this study, the challenge has been defined through a Machine learning concept. machine learning classification was applied to the dataset. Finally, a comparison of the working of these classifiers is presented along with the results, Decision Tree and Support Vector Machine classification models are performing well.
Analisis Sentimen Ulasan Google Maps Kuliner di Bojonegoro Menggunakan Metode Naïve Bayes Fauziyah, Dewi Nur; Sanjaya, Ucta Pradema; Anggraini, Fetrika
The Indonesian Journal of Computer Science Vol. 12 No. 4 (2023): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i4.3319

Abstract

Sentiment analisis merupakan proses menganalisa yang berhubungan dengan sebuah konten atau produk. Analisa terserbut berdasarkan hal hal yang di rasakan oleh seseorang secara subyektif dalam merasakannya. Pada umumnya sentiment analisis ini di tulis oleh penguna dunia maya yang digunakan untuk informasi berdasarkan ulasan atau postingan. Text mining dan Natural language Processing sebuah bidang yang mempunyai irisan yang sama dalam bidang kecerdasan buatan. Ini sangat membantu seseorang dalam mencari nilai sebuah ulasan yang ditulis oleh seseorang memiliki nilai positif atau negative bahkan bisa di nilai netral. Dalam penelitian ini meneliti ulasan usaha kuliner di bojonegoro berdasaran data yang ada di google maps. Untuk metode klasifikasi mengunakan naïve bayes yang pada dasarnya mengunakan penilaian peluang pada setiap atributnya dan di evaluasi dengan confusion matrix. Hasil yang didapatkan dari penelitian ini mendapatkan nilai akurasi 90,28% recall 90,28% dan presisi 89.89%

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