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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) ELKHA : Jurnal Teknik Elektro Jurnal sistem informasi, Teknologi informasi dan komputer Jurnal Informatika dan Teknik Elektro Terapan Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Eksplora Informatika JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Sisfokom (Sistem Informasi dan Komputer) DoubleClick : Journal of Computer and Information Technology Informatik : Jurnal Ilmu Komputer Kurawal - Jurnal Teknologi, Informasi dan Industri JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Jurnal Informatika Global EDUMATIC: Jurnal Pendidikan Informatika JUKI : Jurnal Komputer dan Informatika TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Pendidikan dan Teknologi Indonesia Jumat Informatika: Jurnal Pengabdian Masyarakat Bulletin of Computer Science Research Jurnal Abdi Masyarakat Indonesia Jurnal Pengabdian Masyarakat IPTEK Brilliance: Research of Artificial Intelligence Algoritme Jurnal Mahasiswa Teknik Informatika Informatics and Enginering Dedication Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Nasional Teknologi Komputer The Indonesian Journal of Computer Science Research Innovative: Journal Of Social Science Research MDP Student Conference Jurnal Rekayasa Sistem Informasi dan Teknologi Jurnal Software Engineering and Computational Intelligence Scientific Journal of Informatics Applied Information Technology and Computer Science (AICOMS) Welfare: Jurnal Pengabdian Masyarakat
Muhammad Rizky Pribadi
Universitas Multi Data Palembang
Aerospace Engineering Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Veterinary Other

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Analisis Interaksi Pengguna Sosial Media Sekolah di Palembang Berdasarkan Topik dengan hLDA dan SVM -, Felicia; Pribadi, Muhammad Rizky
JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Vol. 7 No. 2 (2024): Jurnal Teknologi dan Ilmu Komputer Prima (JUTIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jutikomp.v7i2.5536

Abstract

Instagram is a social media that can be used to promote schools by sharing various documentation of school activities, but schools still have difficulty analyzing engagement to find out the audience's interests. This software development aims to identify topics from captions and analyze the like engagement of each topic. 3,900 caption data were collected from five school Instagram accounts in Palembang with Instaloader. The hLDA algorithm is implemented to identify topics from the caption data, and generate a new dataset that gives the topic information of each caption. This dataset was then classified using SVM and SVM-SMOTE. SMOTE is used to overcome class imbalance in order to improve classification results. In the classification process, the dataset is divided into 70% for training and 30% for testing, with evaluation based on F1-Score. The best results were obtained by SVM-SMOTE, with the best F1-Score value from hLDA 3 Level Dataset (13 labels), reaching 95.68% and the lowest value from hLDA 5 Level Dataset (8 labels), reaching 79.43%. Datasets that have more topics give better classification results. Based on the number of likes for each topic in the hLDA 3 Level Dataset, the most popular topic is topic 11, which includes school facilities, student uniforms, and entertainment events. This information can help schools further develop the most liked topics and improve the less liked topics.
ANALYSIS ENGAGEMENT OF INSTAGRAM VISITORS AT UNIVERSITY OF MULTI DATA PALEMBANG BASED ON TOPIC USING LDA Jerin, Nathaniel; Rizky Pribadi, Muhammad; Rusbandi, Rusbandi
Jurnal Rekayasa Sistem Informasi dan Teknologi Vol. 1 No. 2 (2023): November
Publisher : Yayasan Nuraini Ibrahim Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59407/jrsit.v1i2.172

Abstract

Social media has a big impact on everyday life, one of which is to communicate or to get information. Therefore, the development of social media applications makes people use social media applications to find information via the internet. The Instagram applications is one of the most popular social media because it has different topics based on post in the from of images or videos. Therefore, it is very difficult to identify a topic manually. One way to get implied information on social media is through topic modeling. This research was conducted to analyze the application of the LDA method to identify what topics are on Instagram at Multi Data Palembang University. The topics chosen in this study were obtained from LDA based on coherence values. This research uses 2 models, namely random forest and decision tree. Each model tested will produce different accuracy, precision, recall, and f1-score values. Tests were carried out on the LDA labeling dataset and manual labeling, the test results on the LDA labeling dataset were very good using the random forest model with an accuracy values of 78%, precision 80%, recall 66.66%, and f1-score 72.72%.
Analisis Sentimen Komentar Twitter Tentang Perfoma Manchester United Dengan Menggunakan Algoritma Support Vector Machine Dwi Cahyadi, Ambrosius; Rizvi Roshan, Muhamad; Rizky Pribadi, Muhamad
Applied Information Technology and Computer Science (AICOMS) Vol 3 No 1 (2024)
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/aicoms.v3i1.1511

Abstract

Manchester United is one of the largest clubs in the English Premier League with an exceptional history in European and global football. In the 2023/2024 season, Manchester United experienced a very poor season, leading to various positive and negative sentiments from its fans, especially on social media. Sentiment data was gathered from Twitter, where Manchester United fans expressed their opinions regarding the team's performance in the Premier League. This study employs the Support Vector Machine (SVM) method to process and classify data collected from Twitter, aiming to analyze the sentiments of Manchester United fans based on their social media comments. The results indicate that the performance of the Support Vector Machine is relatively poor, achieving an accuracy of 58.73%. This is due to the dataset relying on a single keyword, which led to suboptimal and less complex data, resulting in the Support Vector Machine (SVM) producing relatively low accuracy.
Perbandingan Algoritma SVM dan Naïve Bayes Berbasis SMOTE dalam Analisis Sentimen Komentar Tiktok pada Produk Skincare Liem, Steven; Setiawan, Thomas; Pribadi , M. Rizky
Applied Information Technology and Computer Science (AICOMS) Vol 3 No 1 (2024)
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/aicoms.v3i1.1523

Abstract

This research compares the performance of the Support Vector Machine (SVM) and Naïve Bayes algorithms in sentiment analysis of TikTok comments about skincare products, using the Synthetic Minority Over-sampling Technique (SMOTE) to address data imbalance. The evaluation results indicate that SVM outperforms Naïve Bayes, achieving an accuracy of 59.43% compared to 47.65%. Additionally, SVM excels in the F1 Score metric (60.37% versus 54.74%), although Naïve Bayes demonstrates slightly higher precision (67.96% compared to 62.76%). Therefore, SVM proves to be more effective in classifying sentiment comments, making it the recommended algorithm for sentiment analysis tasks in the skincare product domain on TikTok.
Analisis Sentimen Terhadap Aplikasi Mitra Darat Menggunakan Algoritma Naive Bayes Classifier dan K-Nearest Neighbor Wijaya, Ananda; Rivaldo, Mario; Rizky Pribadi, Muhammad
Applied Information Technology and Computer Science (AICOMS) Vol 3 No 1 (2024)
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/aicoms.v3i1.1542

Abstract

The transportation industry is now an important element as the times develop, especially for today's young generation. Mitra Darat itself is also one of these industries. An application that allows users to easily find out the bus departure schedule that they will take anywhere and anytime on their mobile device. Reviews are definitely given for every app available both positive and negative. With this, we are trying to conduct sentiment analysis research for the Mitra Darat application through reviewing comments from the Google Play Store so that we can identify sentiments related to the use of the Mitra Darat application, as well as provide valuable insights to land transportation service providers to understand user views and improve user services. from the results of our sentiment analysis. The algorithms we use are KNN and NBC. These two algorithms are commonly used by many people because of their expertise in classifying sentiment analysis data and are also popular among researchers. Based on our test results, it can be concluded that our sentiment analysis model designed using the NB algorithm displays higher accuracy performance than KNN. The accuracy of the NB model reached 99.28%, while KNN achieved an accuracy of 80%. This shows that the naïve Bayes algorithm is more suitable to obtain maximum accuracy compared to using k-nearest neighbors.
Klasifikasi Citra Sampah Botol Plastik Jenis HDPE dan PET Menggunakan Algoritma YOLOv7 Purwasih, Opita; Widhiarso, Wijang; Muhammad Rizky Pribadi
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.654

Abstract

The classification of plastic bottle waste, particularly High Density Polyethylene (HDPE) and Polyethylene Terephthalate (PET), remains a challenge in recycling processes due to their similar visual characteristics. Misclassification can lead to a decline in recycled material quality and economic losses in the waste management industry. This research aims to develop an automated image-based classification system to distinguish between HDPE and PET plastic waste using the You Only Look Once version 7 (YOLOv7) object detection algorithm. The dataset consists of plastic bottle images in various physical conditions, annotated with bounding boxes to support model training. The data were split into 70% for training, 20% for validation, and 10% for testing. The best performance was achieved with a batch size of 16 and 100 training epochs, resulting in a precision of 93.9%, recall of 91.6%, and a mean Average Precision (mAP@0.5) of 96.5%. The model demonstrated the ability to accurately classify both types of plastic bottles, even when objects were deformed. These results suggest that the YOLOv7 algorithm is highly capable for implementation in image-based waste classification systems, enhancing sorting efficiency and supporting more sustainable plastic waste management practices.
Metaheuristics Approach for Hyperparameter Tuning of Convolutional Neural Network Purnomo, Hindriyanto; Tad Gonsalves; Evangs Mailoa; Santoso, Fian Julio; Pribadi, Muhammad Rizky
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 3 (2024): June 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i3.5730

Abstract

Deep learning is an artificial intelligence technique that has been used for various tasks. Deep learning performance is determined by its hyperparameter, architecture, and training (connection weight and bias). Finding the right combination of these aspects is very challenging. Convolution neural networks (CNN) is a deep learning method that is commonly used for image classification. It has many hyperparameters; therefore, tuning its hyperparameter is difficult. In this research, a metaheuristic approach is proposed to optimize the hyperparameter of convolution neural networks. Three metaheuristic methods are used in this research: ant colony optimization (ACO), genetic algorithm (GA), and Harmony Search (HS). The metaheuristics methods are used to find the best combination of 8 hyperparameters with 8 options each which creates 1.6. 107 of solution space. The solution space is too large to explore using manual tuning. The Metaheuristics method will bring benefits in terms of finding solutions in the search space more effectively and efficiently. The performance of the metaheuristic methods is evaluated using MNIST datasets. The experiment results show that the accuracy of ACO, GA and HS are 99,7%, 97.7% and 89,9% respectively. The computational times for the ACO, GA and HS algorithms are 27.9 s, 22.3 s, and 56.4 s, respectively. It shows that ACO performs the best among the three algorithms in terms of accuracy, however, its computational time is slightly longer than GA. The results of the experiment reveal that the metaheuristic approach is promising for the hyperparameter tuning of CNN. Future research can be directed toward solving larger problems or improving the metaheuristics operator to improve its performance.
ANALISIS SENTIMEN MASYARAKAT TERHADAP FILM ANIMASI JUMBO DI PLATFORM TIKTOK MENGGUNAKAN ALGORITMA NAÏVE BAYES Se, Abd Rosyiid; Amarullah, Rendy; Pribadi, Rizky
Jurnal Informatika dan Teknik Elektro Terapan Vol. 13 No. 3 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i3.6669

Abstract

Penelitian ini bertujuan untuk menganalisis sentimen masyarakat terhadap film animasi Jumbo di platform media sosial Tiktok dengan menggunakan algoritma Naive Bayes. Data yang digunakan terdiri dari 1000 komentar yang diambil dari platform Tiktok yang kemudian di labeling untuk mengidentifikasi sentimen positif, negatif, dan netral. Data menjalani proses preprocessing sebelum dilakukan klasifikasi yang meliputi pembersihan teks, case folding, tokenisasi, normalisasi, stemming, dan penghapusan stopwords. Penelitian ini juga menggunakan metode Synthetic Minority Oversampling Technique (SMOTE) dalam mengatasi masalah ketika adanya ketidakseimbangan data dalam melakukan klasifikasi. Hasil dari penelitian ini menunjukkan bahwa kinerja dari model Naive Bayes berhasil mengklasifikasikan sentimen dengan tingkat akurasi 63.40%, dengan precision tertinggi tercatat pada sentimen positif yaitu dengan tingkat akurasi 91.80% dan recall tertinggi pada sentimen negatif dengan tingkat akurasi 100%. Meskipun demikian, model ini masih menunjukkan performa rendah pada kelas netral dengan tingkat akurasi recall hanya 15.44%. Penelitian ini dapat diharapkan memberikan wawasan bagi pengembang film mengenai pandangan masyarakat terhadap film animasi Jumbo.
Comparison Algorithm Backpropagation And Support Vector Machine On The Introduction of Corn Seed Type Yunarto, Yunarto; Pribadi, Muhammad Rizky; Irsyad, Hafiz
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 1 No 1 (2020): Oktober 2021 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1022.778 KB) | DOI: 10.35957/algoritme.v1i1.539

Abstract

Jagung termasuk tumbuhan biji-bijian seperti padi, gandum, sorgum yang dikonsumsisebagai sumber makanan pokok di Amerika dan beberapa wilayah di Indonesia seperti Madura,Nusa Tenggara Timur, Sulawesi dan Jawa Tengah. Jagung biji memiliki banyak jenis, makadari itulah jika jagung biji tersebut tercampur akan susah untuk dibedakan. Tujuan daripenelitian ini adalah untuk mengenali biji jagung tersebut. Jenis biji jagung yang digunakanadalah jagung merah pozole, jagung pipil, jagung putih dan jagung warna-warni yang difotomenggunakan camera 16MP dengan jarak pengambilan foto 10cm antara kamera dengan objekjagung. Metode pengenalan yang digunakan adalah algoritma backpropagation dan support vector machine, sedangkan untuk ekstraksi fitur menggunakan metode GLCM(Gray Co-occurence Matrix) yang terdiri dari Contrast, energy, homogeneity, dan correlation. Pada perhitungan dengan confusion matrix hasil tertinggi didapatkan pada algoritmabackpropagation dengan rata-rata accuracy 97,5, rata-rata precision 95% dan rata-rata recallsebesar 95,1% dibandingkan dengan algoritma support vector machine yang hanya mendapatrata-rata accuracy 97,1%, rata-rata precision 93,3% dan rata-rata recall sebesar 95%.
Perancangan Antarmuka Aplikasi Serve-U Dengan Metode Design Thinking Suparto, Adrian; Clement, Michael Joy; Pratama, Brilliant Chandra; Ferliansyah, Fernando; M Lazuardi Ferdillian; Pribadi, Muhammad Rizky
Jurnal Nasional Teknologi Komputer Vol 4 No 3 (2024): Volume 4 Nomor 3 Juli 2024
Publisher : CV. Hawari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61306/jnastek.v4i3.146

Abstract

Era digital telah memberkati orang-orang dengan alat-alat luar biasa yang dapat membuat hidup mereka jauh lebih mudah, dan desain UI/UX (User Interface/User Experience) juga merupakan salah satunya. Dengan menempatkan pengguna sebagai pusatnya, metodologi Design Thinking membantu mengembangkan solusi yang efektif. Salah satu masalah global yang paling mendesak, yang bahkan lebih umum terjadi di negara-negara berkembang, adalah pengangguran dan ketidakcocokan pekerjaan yang terjadi sebagai akibat dari kesenjangan antara peluang kerja yang tersedia dan keterampilan pencari kerja. Teknologi telah menawarkan solusi dengan menyediakan platform yang dapat diakses oleh para penyedia jasa tanpa memandang sertifikasi akademis mereka. Dikembangkan dengan menggunakan Design Thinking, Serve-U memungkinkan pengguna untuk mengidentifikasi layanan mikro yang penting seperti layanan transportasi, kurir, atau pembantu rumah tangga yang dibutuhkan di daerah perkotaan. Makalah ini memberikan prototype yang mudah digunakan dan efektif antara pengguna dan penyedia layanan untuk melakukan pekerjaan mikro, yang mulai mengatasi masalah perkotaan. Kata Kunci: Design Thinking, ketenagakerjaan mikro, pengalaman pengguna, aplikasi mobile, layanan perkotaan.
Co-Authors -, Felicia Adi Saputra Aditya Al Assad Adrian Chen Ahmad Dumyati Ahmad Zaky Nadimsyah Alwin Marcellino Amarullah, Rendy Ampu Syura Andreas Andreas Andreas Danny Agus W Andreas Saputra Andrian Wijaya Angel Kelly Angelica, Steffanie Asyraq, Cerwyn Bakti Ananda Fernando Bautista, Christian Bella Jenni Ourelia Boy Putra Calvin Bertnas Valentino Calvin Saputra Carissa Maharani Chandra Caroline, Fellycia Chandra Saputra Clara Meyhazlinda Putri Clement, Michael Joy Daniel Daniel Daniel Johan Daniel Wijaya Darwin Saputra David Sebastian Dedy Hermanto Desta Rahman Theja Desy Iba Ricoida Devina Suryanto, Serenity Dicky Ryanto Fernandes Diva Putri Kynta Dwi Apriyanti Sastika Dwi Cahyadi, Ambrosius Effendi pratama, Samuel Egi Fransisco Saputra Eka Puji Widiyanto Evangs Mailoa Evi Maria Fadhil Sa'adat Fajar Ariansyah, Muhammad Farisi, Ahmad Farisi, Ahmad Fathimah Azzahra Feliansyah, Fernando Felicia Felicia Fellyca Effendi Feriyanto Feriyanto Ferliansyah, Fernando Fernandi Indi Nizar G Fernando Fernando Fernando Namas Fionna Caroline Florence Renaldo Frans Bachtiar Fransiskus Daniel Chandra Frisky Wijaya Genisshanda Nabila Matari Geraldo Wilson Gerry Christian Pilipus Gunawan, Michael Hafidz Irsyad Hafiz Irsyad Hansen Hansen Hendrawan, Malvin Hendry Hindriyanto Dwi Purnomo Hujaya, Alvin Ilham Indra Hidayat Imelia Dwinora Cahyati Indi Nizar G, Fernandi Ivan Luthfi Laksono Jackie Wijaya Jasen Jonathan Ja`Far Ja`Far Jelvin Krisna Putra Jerin, Nathaniel Kasanova, Sinyo Kelvin Dwi Wahyudi Kevin agustria zahri Kevin Andreas KGS M Ammar Yazid Kurniawan, Ricky Arie Kusuma, Aditya Ali Laksana, Jovansa Putra Laurentius Ricardo Wijaya Leo Chandra Leonardo Yahya Liem, Steven Lin, Valen Julyo Armando Davincy Lipi Amanda Putra Lucretia, Jolyn M Lazuardi Ferdillian Michael michael Wijaya Millenia Mudita Chandra Muhammad Abdul Azizul Hakim Muhammad Alfa Rizi Muhammad Azril Fahrezi Muhammad Dafhi Mayrizkiy Muhammad Dody Muhammad Fadli Muhammad Hamdandi Muhammad Naufal Anugrah Muhammad Redho Saputra Muhammad Reyza Nirwana Muhammad Robi, Muhammad Nabila Syiva Altarisa Nabilah Dayanah Nathacia Lais Naufal Akbar Neilsen Nicholas Komah Nicolas Jacky Pratama Hasan Nova Ariansyah Pambudi, Readysna Krisna Paula, Bebin Pebrian, Hafizh Peter Reynard Susanto Pibriana, Desi Prasetyo, Zavier Billy Pratama, Brilliant Chandra Purwasih, Opita Putra Laksana, Jovansa Putri, Agnes Anastasia Regian batistuta, Putra Reza Satria Rika Maulina Riki Chandra Rio Ferdynand Riska Fajriati Rivaldo Therino Elevan Rivaldo, Mario Riza Umami Rizky Kurniawan Rizvi Roshan, Muhamad Roby Julian Romi Laxi Ronaldo Putra Rusbandi rusbandi rusbandi, rusbandi Salwa Fakhira Imletta San Gabriel Vanness Kenrick Erwi Sanila Maharani Santoso, Fian Julio Saputra Edika, Nelson Sardika, Ricky Putra Se, Abd Rosyiid Setiawan, Thomas Shela, Shela Sherdian Djunaidi Sinshevan Viswanatan Kravizt Erwi Sonia Sonia Sri Yulianto Joko Prasetyo Stephanie Stephanie Stephen Setyawan Steven Tribethran Suparto, Adrian Suryasatria Trihadaru Sutarto Wijono Syahrani Nur Hakim Syifa Wahyuni Tad Gonsalves Tangguh Prana Welas Sukma Vannes Wijaya Vanness Bee Vincent Vincent Virgiansyah, Muhammad Rifqi Wijang Widhiarso Wijaya, Ananda Wilcent, Wilcent William Wijaya Yennica Valentine Hagunawan Yohanes Andika Dharma Yohanes Fransisco Mardi Chandra Yohannes, Yohannes Yoko Saputra Dewa Yosefa Camilia Moniung Yunarto Yunarto, Yunarto `Adelia Anjelina