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All Journal International Journal of Advances in Intelligent Informatics INFORMAL: Informatics Journal Wikrama Parahita : Jurnal Pengabdian Masyarakat MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Indonesian Journal of Electrical Engineering and Computer Science Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) Jurnal Pengabdian Pada Masyarakat
Dharmawan, Tio
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Penguatan Kompetensi Computational Thinking dalam Pembelajaran IPA Melalui Perancangan Pembelajaran Argumentasi Konstruktivis Bukhori, Saiful; Retnani, Windi Eka Yulia; Putra, Januar Adi; Dharmawan, Tio
Wikrama Parahita : Jurnal Pengabdian Masyarakat Vol. 8 No. 1 (2024): Mei 2024
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/jpmwp.v8i1.7249

Abstract

Argumentasi merupakan keterampilan kritis yang perlu dibangun pada siswa usia SD, dan perlu dikembangkan pada siswa usia sekolah menengah. Secara teoritis, siswa dengan usia muda seharusnya mampu memahami dan mem­bangun argumen, akan tetapi berdasarkan bukti empiris belum mendukung harapan tersebut. Kondisi ini juga terjadi pada siswa di SD sekitar desa jelbuk. Pada pengabdian kepada masyarakat ini dirancang dan diimplementasikan penguatan kompetensi computational thinking (CT) dalam pembelajaran IPA melalui perancangan pembelajaran argumentasi konstruktivis. Penguatan kompetensi CT pada pengabdian ini dilakukan menggunakan konsep CT-Argumentasi. CT menyediakan proses yang diperlukan untuk merumuskan argumen, sedangkan argumen memanfaatkan dan menerapkan keterampil­an CT melalui penalaran logis. CT yang diberikan kepada siswa dalam peng­abd­ian kepada masyarakat ini mengacu pada 4 tahapan yaitu: decompos­ition, pattern recognition, abstraction, dan algorithm. Hasil dari pengabdian kepada masyarakat ini dapat meningkatkan pemahaman siswa terhadap materi sebesar 40%. Pengabdian ini juga mengenalkan CT kepada siswa dan guru, dan dapat meningkatkan keterampilan menyelesaikan permasalahan dengan CT dibuktikan dengan rata-rata 30% siswa yang hadir angkat tangan dan dapat menjawab dengan benar, ketika diberi pertanyaan dengan permasalahan terbuka yang diambil dari contoh soal di situs web resmi Bebras Indonesia.
Seasonal meat stock demand used comparison of performance smoothing-average forecasting Tundo, Tundo; Saifullah, Shoffan; Dharmawan, Tio; Junaidi, Junaidi; Devia, Elmi
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp425-433

Abstract

Seasonal patterns significantly influence the demand for beef stock, especially in rural areas that rely on natural feed. Accurate forecasting is essential for managing this demand due to beef's status as a government-regulated nutritional commodity. Food production, consumption, and income levels affect the demand for beef stocks. This research aims to identify the most precise forecasting method for predicting future beef stock needs. We evaluated multiple techniques, including single exponential smoothing (SES), double exponential smoothing (DES), single moving average (SMA), and double moving average (DMA), using the mean absolute percentage error (MAPE) metric, focusing specifically on beef supplies in Pemalang. The results indicated that the DMA method achieved the highest accuracy with a MAPE value of 5.993% at the 4th -order parameter. Additionally, increasing the data volume improved forecasting accuracy, demonstrating the effectiveness of the DMA method for beef stock prediction.
Sentiment Analysis of Skincare Active Ingredient Topics using Latent Dirichlet Allocation and InSet Lexicon on Twitter Social Media Nuarie, Aurila; Adiwijaya, Nelly Oktavia; Dharmawan, Tio
INFORMAL: Informatics Journal Vol 9 No 3 (2024): Informatics Journal (INFORMAL)
Publisher : Faculty of Computer Science, University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/isj.v9i3.46116

Abstract

The cosmetic industry, encompassing skincare, underwent a growth rate of up to 9.61%, as indicated by data from the Central Statistics Agency (BPS). With the ongoing expansion of the cosmetic sector, the production of products, particularly those featuring active ingredients in skincare, increased accordingly. Consequently, the utilization of these active ingredients witnessed an upward trend. Twitter data pertaining to active skincare ingredients was collected, forming a substantial dataset that required methods for analyzing topics and opinions.To identify latent topic information, topic modeling using Latent Dirichlet Allocation (LDA) was employed. Prior to conducting topic modeling, clustering was initially performed using K-Means to facilitate the categorization of the extensive dataset into more specific data groups. Subsequently, sentiment analysis was carried out using the InSet Lexicon. The research resulted in four clusters, each of which underwent topic modeling with LDA.Cluster 1 unveiled a topic focusing on the content of alpha arbutin, with sentiment results of 42.5% positive, 45% negative, and 12.5% neutral. Cluster 2 centered around the content of reinol and AHA BHA, with sentiment results of 41.36% positive, 46.99% negative, and 12.13% neutral. Cluster 3 delved into the content of salicylic acid and hyaluronic acid, with sentiment results of 40.57% positive, 42.62% negative, and 16.80% neutral. Lastly, Cluster 4 discussed the clay mask "Skintific" containing mugwort, with sentiment results of 41.67% positive, 43.94% negative, and 14.39% neutral.This research is anticipated to be beneficial and can be utilized by the skincare industry to update the company's business strategies.
Pelatihan Penggunaan Platform Canva untuk Optimalisasi Desain Grafis bagi Pengrajin Papan Bunga Akrilik Bukhori, Saiful; Bukhori, Hilmi Aziz; Dharmawan, Tio; Prasetyo, Beny; R., Windi Eka Y.
Jurnal Pengabdian Pada Masyarakat Vol 10 No 1 (2025): Jurnal Pengabdian Pada Masyarakat
Publisher : Universitas Mathla'ul Anwar Banten

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30653/jppm.v10i1.1105

Abstract

Pengrajin papan bunga akrilik, terutama yang membuat papan bunga beserta tulisan ucapan selamat pada momen spesial dengan desain khusus sering menghadapi beberapa permasalahan dalam desain tulisannya antara lain adalah: kualitas tulisan dan keterbacaan, keterampilan teknikal terutama dalam menentukan jenis dan ukuran tulisan, kreativitas dan desain sesuai dengan permintaan klien, efisiensi waktu pembuatan, serta estetika dan fungsi. Permasalahan ini disebabkan karena belum memiliki pengetahuan tentang cara mengelola tulisan ucapan selamat pada momen spesial dan masih menggunakan cara manual. Pengabdian kepada masyarakat ini bertujuan untuk memberikan keterampilan pada pengrajin papan bunga akrilik di Malang selaku peserta pelatihan dalam mengelola tulisan ucapan momen spesial pada papan bunga akrilik dengan menggunakan platform desain grafis online canva. Berdasarkan hasil evaluasi pengabdian kepada masyarakat yang kemudian dianalisis secara sistematis menunjukkan adanya peningkatan pengetahuan pengrajin papan bunga akrilik di Malang dalam hal manajemen desain tulisan pada papan bunga akrilik sekaligus mempengaruhi produk desain papan bunga secara keseluruhan sebelum dan sesudah dilaksanakan pelatihan. Acrylic flower board craftsmen, especially those who make flower boards with congratulatory writings on special moments with special designs often face several problems in their writing designs, including: writing quality and readability, technical skills especially in determining the type and size of writing, creativity and design according to client requests, efficiency of production time, and aesthetics and function. This problem is caused by not having knowledge about how to manage congratulatory writings on special moments and still using manual methods. This community service aims to provide skills to acrylic flower board craftsmen in Malang as training participants in managing special greeting writings on acrylic flower boards using the canva online graphic design platform. Based on the results of the community Service evaluation which were then analyzed systematically, it showed an increase in the knowledge of acrylic flower board craftsmen in Malang in terms of writing design management on acrylic flower boards as well as influencing the overall flower board design product before and after the training was carried out.
Gender classification performance optimization based on facial images using LBG-VQ and MB-LBP Hakim, Faruq Abdul; Dharmawan, Tio; Hidayat, Muhamad Arief
International Journal of Advances in Intelligent Informatics Vol 11, No 1 (2025): February 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i1.1827

Abstract

In the computer vision and machine learning field, especially for gender classification based on facial images, feature extraction is one of the inseparable parts. Various features can be extracted from images, including texture features. Several prior studies show that the Linde Buzo gray vector quantization (LBG-VQ) and Multi-block local binary pattern (MB-LBP) methods can extract texture features from images. The LBG-VQ produces less optimal performance in gender classification on the FEI facial images dataset. On the other hand, the MB-LBP produces more optimal performance when applied to the FERET facial images dataset. Therefore, this study was conducted to discover the gender classification performance when the LBG-VQ and MB-LBP methods are implemented independently or in combination on the FEI facial images dataset. Three preprocessing stages are involved before extracting images' features: noise removal, illumination adjustment, and image conversion from RGB to grayscale. The extracted features are then used as training material for several classification methods, namely Naïve Bayes, SVM, KNN, Random Forest, and Logistic Regression. Then, the K-Fold Cross Validation method is used to evaluate the trained models. This study discovered that the implementation of MB-LBP tends to show a performance improvement compared to the LBG-VQ. Furthermore, the most optimal classification model, with a performance of 91.928%, was formed by implementing Logistic Regression with MB-LBP on LBG-VQ quantized images. In conclusion, this study successfully formed an optimized gender classification model based on the FEI facial images dataset.
Pengaruh Penggunaan Emoji Pada Tingkat Akurasi Sentimen Di Twitter Menggunakan Metode Support Vector Machine Dharmawan, Tio; Kinanti, Virli Galuh; Maududie, Achmad
Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) 2025: SNESTIK V
Publisher : Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/p.snestik.2025.7046

Abstract

Opinions and preferences expressed on social media and microblogging services are very important for sentiment analysis. A Support Vector Machine (SVM) is a learning system that uses a hypothetical space in the form of a linear function in a high dimensional feature space and applies a learning bias derived from statistical learning theory. The accuracy results obtained by the Support Vector Machine method from the first topic, namely booster vaccines as a homecoming requirement, were 65% for text only and 69% for text containing emoji. The accuracy results for the second discussion topic, namely demonstrations against Jokowi for 3 periods, were 79% for text only and 82% for text containing emoji. As for the third topic regarding the scarcity of cooking oil and rising fuel prices, the accuracy obtained is 74% for text only and 76% for text containing emojis.
Optimasi Model Rekomendasi Topik Skripsi berdasarkan Performa Akademik Mahasiswa menggunakan SMOTE Adiwijaya, Nelly Oktavia; Al Abror, Muhammad Farhan; Dharmawan, Tio; Hidayat, Muhamad Arief
Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) 2025: SNESTIK V
Publisher : Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/p.snestik.2025.7055

Abstract

Sekitar 68% mahasiswa mengalami keterlambatan dalam menyelesaikan skripsi yang mengindikasikan adanya kesulitan dalam penentuan topik penelitian sesuai dengan minat dan keahlian.Ketidaksesuaian ini seringkali disebabkan kurangnya pemahaman mahasiswa terhadap kemampuan akademik yang dimiliki. Hal ini berdampak signifikan pada keterlambatan kelulusan mahasiswa.Penelitian ini bertujuan mengatasi permasalahan tersebut dengan membangun model klasifikasi untuk membantu mahasiswa dalam menentukan topik skripsi berdasarkan kemampuan akademik mereka. Indikator yang digunakan berupa transkrip nilai mata kuliah mahasiswa dari semester 1 hingga semester 6. Penelitian ini menggunakan metode Feature Selection dan SMOTE sebelum dilakukan pemodelan untuk meningkatkan kualitas data. Dua algoritma Support Vector Machine (SVM) dengan kernel RBF dan Naive Bayes tipe kategorikal digunakan untuk membangun model klasifikasi. Berdasarkan hasil analisis yang diperoleh bahwa penerapan SMOTE untuk penanganan data sebelum diklasifikasi berpengaruh sangat baik terhadap hasil akurasi. Algoritma Support Vector Machine dengan kernel RBF memberikan akurasi tertinggi sebesar 96.81% sedangkan Naive Bayes tipe Categorical menghasilkan akurasi 83.75%. Hasil penelitian ini memberikan solusi praktis bagi mahasiswa dalam memilih topik skripsi yang relevan dengan kemampuan mereka dimana mata kuliah yang terkait dengan setiap topik skripsi dapat berbeda-beda untuk masing-masing mahasiswa.
Gender Classification Using Viola Jones, Orthogonal Difference Local Binary Pattern and Principal Component Analysis Mukminin, Muhammad Amirul; Dharmawan, Tio; Hidayat, Muhamad Arief
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 3 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i3.3879

Abstract

Facial recognition is currently a widely discussed topic, particularly in the context of gender classification. Facial recognition by computers is more complex and time-consuming compared to humans. There is ongoing research on facial feature extraction for gender classification. Geometry and texture features are effective for gender classification. This study aimed to combine these two features to improve the accuracy of gender classification. This research used the Viola-Jones and Orthogonal Difference Local Binary Pattern (OD-LBP) methods for feature extraction. The Viola-Jones algorithm faces issues in facial detection, leading to outliers in geometry features. At the same time, OD-LBP is a new descriptor capable of addressing pose, lighting, and expression variations. Therefore, this research attempts to utilize OD-LBP for gender classification. The dataset used was FERET, which contained various lighting variations, making OD-LBP suitable for addressing this challenge. Random Forest and Backpropagation were employed for classification. This research demonstrates that combining these two features is effective for gender classification using Backpropagation, achieving an accuracy of 93%. This confirms the superiority of the proposed method over single-feature extraction methods.
Co-Authors Achmad Maududie Al Abror, Muhammad Farhan Bukhori, Hilmi Aziz Devia, Elmi Hakim, Faruq Abdul Hidayat, Muhamad Arief januar adi putra, januar adi Junaidi Junaidi Kinanti, Virli Galuh Mukminin, Muhammad Amirul Nuarie, Aurila Oktavia, Nelly R., Windi Eka Y. Saiful Bukhori Saifullah, Shoffan Tundo, Tundo Windi Eka Yulia Retnani