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All Journal JOIV : International Journal on Informatics Visualization Journal of Robotics and Control (JRC) Humanism : Jurnal Pengabdian Masyarakat International Journal of Robotics and Control Systems DECODE: Jurnal Pendidikan Teknologi Informasi Journal of Software Engineering and Information System (SEIS) Jurnal Riset Sistem dan Teknologi Informasi Jurnal Pengabdian kepada Masyarakat (PEMAS)
Dianda Rifaldi
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Religion Aerospace Engineering Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Public Health Social Sciences Other

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Teknik Preprocessing Pada Text Mining Menggunakan Data Tweet “Mental Health” Dianda Rifaldi; Abdul Fadlil; Herman
Decode: Jurnal Pendidikan Teknologi Informasi Vol. 3 No. 2: SEPTEMBER 2023
Publisher : Program Studi Pendidikan Teknologi Infromasi UMK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51454/decode.v3i2.131

Abstract

Indonesia merupakan salah satu negara di kategorikan pengguna media sosial twitter terbanyak yaitu mencapai 18,45 pada periode januari tahun 2022 juta pengguna sehingga data pada twitter dapat digunakan dalam melakukan bebagai penelitian. Data penelitian ini menggunakan data media sosial twitter yang diambil dengan metode crawling dan mendapatkan data sebanyak 9739 yang diambil dari tanggal 19 oktober 2022 sampai 4 desember 2022 dengan menggunakan keyword “mental health”. Data hasil crawling masih berbentuk mentah dan tidak terstruktur, sehingga perlu dilakukan preprocessing agar data dapat di proses ke tahap selanjutnya dan menghasilkan data yang dapat diolah menggunakan tools pengolah data. Tujuan penelitian ini adalah melakukan preprocessing pada data yang sudah diperoleh melalui twitter. Pengolahan data menggunakan model machine learning diperlukan tahap persiapan data yaitu dengan melakukan preprocessing agar data yang digunakan dapat diolah dengan baik. hasil penelitian ini adalah data yang melewati tahap preprocessing telah berbentuk kata dasar dan siap diolah untuk melakukan penelitian terkait mental health. Beberapa tahapan yang dilakukan pada preprocessing yaitu perubahan bentuk kata dasar, menghapus kata yang tidak penting, menghapus imbuhan, dan konjungsi dari dokumen tweet. Selanjutnya data yang telah melewati tahap preprocessing siap untuk dilakukan pembuatan model analisis sentimen yang berguna dalam pengambilan keputusan terhadap permasalahan tersebut.
Pengenalan Dan Pelatihan UI/UX Serta Jenjang Karir Di Masa Depan untuk Siswa Siswi SMK Informatika Wonosobo Abdul Fadlil; Murinto; Asno Azzawagama Firdaus; Dianda Rifaldi
Humanism : Jurnal Pengabdian Masyarakat Vol 4 No 3 (2023): Desember
Publisher : Universitas Muhammadiyah Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30651/hm.v4i3.20285

Abstract

Artikel ini menyajikan kegiatan pengabdian yang dilaksanakan pada 12 Juni 2023 di SMK Informatika Wonosobo, Jawa Tengah. Kegiatan tersebut difokuskan pada pengenalan desain UI/UX dan pelatihan terkait desain UI/UX untuk membantu siswa mempersiapkan karir di bidang tersebut di masa depan. Sebanyak 20 orang siswa ikut serta dalam kegiatan ini yang didampingi oleh pihak sekolah. Peserta menunjukkan antusiasme yang tinggi selama kegiatan berlangsung. Kegiatan berupa sosialisasi dan tanya jawab hingga praktik langsung ini memang baru kali pertama diselenggarakan pada SMK Informatika Wonosobo tersebut sehingga siswa belum memiliki pemahaman mengenai desain UI/UX. Hal tersebut terlihat dari peningkatan skor akhir yang signifikan dalam evaluasi pra dan pasca pembekalan menggunakan pre test dan post test dengan metode perhitungan likert. Skor akhir meningkat dari 44,2% pada pre test menjadi 93,6% pada post test. Hasil ini menunjukkan bahwa kegiatan pengabdian ini berhasil meningkatkan pemahaman dan pengetahuan peserta dalam bidang desain UI/UX. Pihak sekolah mengharapkan kegiatan serupa dapat tetap dilaksanakan di SMK Informatika Wonosobo guna meningkatkan pengetahuan dan pemahaman siswa mengenai dunia kerja.
Application of Machine Learning in Healthcare and Medicine: A Review Furizal, Furizal; Ma'arif, Alfian; Rifaldi, Dianda
Journal of Robotics and Control (JRC) Vol 4, No 5 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i5.19640

Abstract

This extensive literature review investigates the integration of Machine Learning (ML) into the healthcare sector, uncovering its potential, challenges, and strategic resolutions. The main objective is to comprehensively explore how ML is incorporated into medical practices, demonstrate its impact, and provide relevant solutions. The research motivation stems from the necessity to comprehend the convergence of ML and healthcare services, given its intricate implications. Through meticulous analysis of existing research, this method elucidates the broad spectrum of ML applications in disease prediction and personalized treatment. The research's precision lies in dissecting methodologies, scrutinizing studies, and extrapolating critical insights. The article establishes that ML has succeeded in various aspects of medical care. In certain studies, ML algorithms, especially Convolutional Neural Networks (CNNs), have achieved high accuracy in diagnosing diseases such as lung cancer, colorectal cancer, brain tumors, and breast tumors. Apart from CNNs, other algorithms like SVM, RF, k-NN, and DT have also proven effective. Evaluations based on accuracy and F1-score indicate satisfactory results, with some studies exceeding 90% accuracy. This principal finding underscores the impressive accuracy of ML algorithms in diagnosing diverse medical conditions. This outcome signifies the transformative potential of ML in reshaping conventional diagnostic techniques. Discussions revolve around challenges like data quality, security risks, potential misinterpretations, and obstacles in integrating ML into clinical realms. To mitigate these, multifaceted solutions are proposed, encompassing standardized data formats, robust encryption, model interpretation, clinician training, and stakeholder collaboration.
Comparison of Convolutional Neural Networks and Support Vector Machines on Medical Data: A Review Furizal, Furizal; Ma'arif, Alfian; Rifaldi, Dianda; Firdaus, Asno Azzawagama
International Journal of Robotics and Control Systems Vol 4, No 1 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i1.1375

Abstract

Medical image processing has become an integral part of disease diagnosis, where technological advancements have brought significant changes to this approach. In this review, a comprehensive comparison between Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) in processing medical images is conducted. Automated medical analysis is becoming increasingly important due to issues of subjectivity in manual diagnosis and potential treatment delays. This research aims to compare the performance of Machine Learning (ML) in medical contexts using MRI, CT scan, and X-ray data. The comparison includes the accuracy rates of CNN and SVM algorithms, sourced from various studies conducted between 2018 and 2022. The results of the comparison show that CNN has higher average accuracy in processing MRI and X-ray data, with average values of 98.05% and 97.27%, respectively. On the other hand, SVM exhibits higher average accuracy for CT scan data, reaching 91.78%. However, overall, CNN achieves an average accuracy of 95.58%, while SVM's average accuracy is at 94.72%. These findings indicate that both algorithms perform well in processing medical data with high accuracy. Although based on these average accuracy rates, CNN demonstrates slightly better capabilities than SVM. Further research and development of more complex models are expected to continue improving the effectiveness of both approaches in disease diagnosis and patient care in the future.
Implementation of Word Trends Using a Machine Learning Approach with TF-IDF and Latent Dirichlet Allocation Rifaldi, Dianda; Fadlil, Abdul; Herman, -
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.2452

Abstract

In today's technological age, the prevalence of social media has become ubiquitous, facilitating the easy dissemination of information and communication. This has led to the uploading of various content, including opinions on mental health, particularly in Indonesia. Mental health refers to an individual's emotional, psychological, and social well-being, commonly affecting individuals from adolescence to adulthood. This research utilized Twitter data on mental health issues gathered from October to November 2022, employing TF-IDF and Latent Dirichlet Allocation (LDA) to conduct topic modeling for word trend analysis based on user-generated content. The sentiment analysis concept was used to label text as either negative or positive sentiment. Subsequently, TF-IDF weighed the word frequency in the documents/tweets, categorizing the data based on the resulting sentiments. Manual labeling ensured accuracy, avoiding potential errors from libraries provided in the Indonesian language. Employing these two topic modeling techniques, conclusions were drawn for each concept, aiming to identify word trends, mainly focusing on mental health discourse within Twitter user-generated content. Results indicated the synchronicity of the keyword 'mental health' with word trends generated by LDA. At the same time, TF-IDF produced word trends based on positive and negative labels, revealing commonly used terms by Twitter users to express these concerns. Furthermore, subsequent research can be experimented by comparing topic modeling techniques using Latent Semantic Allocation (LSA), Probabilistic Latent Semantic Analysis (pLSA), and Hierarchical Dirichlet Process (HDP), where LSA and pLSA present approaches closely aligned with LDA.
Evaluasi Sentimen Pengguna ChatGPT Menggunakan Naive Bayes: Tinjauan dari Confusion Matrix dan Classification Report Dianda Rifaldi; Tri Stiyo Famuji; Bella Okta Sari Miranda; Fauzan Purma Ramadhan; Iriene Putri Mulyadi; Vanji Saputra6; Fanani, Galih Pramuja Inngam
Jurnal Riset Sistem dan Teknologi Informasi Vol. 3 No. 2 (2025): Jurnal Riset Sistem dan Teknologi Informasi (RESTIA)
Publisher : Universitas Aisyiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30787/restia.v3i2.1990

Abstract

The development of artificial intelligence (AI) technology, particularly in natural language processing (NLP), has led to various innovations, including ChatGPT. Its growing popularity highlights the need for user sentiment analysis. This study evaluates user sentiment toward ChatGPT using the Naive Bayes algorithm. The dataset, obtained from Kaggle, consists of 500 labeled English tweets categorized as positive, neutral, or negative. The process involved text preprocessing, TF-IDF feature extraction, data splitting (80% training, 20% testing), and model training. The results show an accuracy of 56%, with the highest f1-score in the negative class (0.67) and the lowest in the neutral class (0.38). The model exhibits classification imbalance, with high precision but low recall in the neutral class, and high recall but low precision in the positive class. The confusion matrix further confirms frequent misclassifications between classes. These findings reflect the limitations of Naive Bayes in handling contextual relationships in text data. Improvements can be achieved through data balancing, enhanced NLP-based feature representation, and the application of more complex classification algorithms.
Pelatihan Digital Marketing Berbasis AI dan Canva bagi Siswa SMK di Klaten untuk Meningkatkan Kompetensi Promosi Produk Fanani, Galih; Rifaldi, Dianda; Tristanti, Novi
Jurnal Pengabdian kepada Masyarakat (PEMAS) Vol. 2 No. 2 (2025): Mei 2025
Publisher : Yayasan Ran Edu Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63866/pemas.v2i2.97

Abstract

Perkembangan teknologi digital menuntut siswa Sekolah Menengah Kejuruan (SMK) menguasai keterampilan digital marketing yang relevan dengan kebutuhan industri. Observasi awal di SMK Negeri 1 Trucuk, Klaten, menunjukkan bahwa sebagian besar siswa belum memahami strategi promosi berbasis teknologi, khususnya pemanfaatan Artificial Intelligence (AI) dan Canva. Penelitian ini bertujuan meningkatkan kompetensi promosi produk melalui pelatihan digital marketing berbasis AI dan Canva. Penelitian menggunakan metode experiential learning dengan tahapan pretest, materi, praktik pembuatan desain dan copywriting, diskusi kelompok, serta evaluasi posttest. Sebanyak 20 peserta (siswa SMK) yang aktif dalam kewirausahaan sekolah terlibat sebagai peserta. Hasil evaluasi menunjukkan rata-rata skor posttest meningkat 45% dibandingkan pretest. Peserta mampu menghasilkan konten promosi lebih kreatif, komunikatif, dan sesuai target pasar, serta memahami integrasi teknologi digital dalam strategi pemasaran. Pelatihan ini efektif sebagai model pengembangan literasi digital dan keterampilan promosi produk bagi siswa SMK di era industri 4.0.
Pengenalan Dan Pelatihan UI/UX Serta Jenjang Karir Di Masa Depan untuk Siswa Siswi SMK Informatika Wonosobo Fadlil, Abdul; Murinto; Firdaus, Asno Azzawagama; Rifaldi, Dianda
Humanism : Jurnal Pengabdian Masyarakat Vol 4 No 3 (2023): Desember
Publisher : Universitas Muhammadiyah Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30651/hm.v4i3.20285

Abstract

Artikel ini menyajikan kegiatan pengabdian yang dilaksanakan pada 12 Juni 2023 di SMK Informatika Wonosobo, Jawa Tengah. Kegiatan tersebut difokuskan pada pengenalan desain UI/UX dan pelatihan terkait desain UI/UX untuk membantu siswa mempersiapkan karir di bidang tersebut di masa depan. Sebanyak 20 orang siswa ikut serta dalam kegiatan ini yang didampingi oleh pihak sekolah. Peserta menunjukkan antusiasme yang tinggi selama kegiatan berlangsung. Kegiatan berupa sosialisasi dan tanya jawab hingga praktik langsung ini memang baru kali pertama diselenggarakan pada SMK Informatika Wonosobo tersebut sehingga siswa belum memiliki pemahaman mengenai desain UI/UX. Hal tersebut terlihat dari peningkatan skor akhir yang signifikan dalam evaluasi pra dan pasca pembekalan menggunakan pre test dan post test dengan metode perhitungan likert. Skor akhir meningkat dari 44,2% pada pre test menjadi 93,6% pada post test. Hasil ini menunjukkan bahwa kegiatan pengabdian ini berhasil meningkatkan pemahaman dan pengetahuan peserta dalam bidang desain UI/UX. Pihak sekolah mengharapkan kegiatan serupa dapat tetap dilaksanakan di SMK Informatika Wonosobo guna meningkatkan pengetahuan dan pemahaman siswa mengenai dunia kerja.
EKSPLORASI SENTIMEN PENGGUNA X TERHADAP ISU KESEHATAN MENTAL BERBASIS MACHINE LEARNING Rifaldi, Dianda; Famuji, Tri Stiyo; Fanani, Galih Pramuja Inngam; Ramadhan, Fauzan Purma; Mulyadi, Iriene Putri; Saputra, Vanji
Jurnal Rekayasa Perangkat Lunak dan Sistem Informasi Vol. 5 No. 2 (2025)
Publisher : Department of Information System Muhammadiyah University of Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/seis.v5i2.9594

Abstract

Mental health has become an increasingly relevant topic in the digital era, particularly on social media platforms such as X, which serve as public spaces for expressing opinions and sharing personal experiences. This study aims to analyze public sentiment toward mental health topics on Twitter using the Multinomial Naive Bayes algorithm. Data were collected from tweets containing mental health-related keywords and processed through text cleaning and feature extraction using the TF-IDF method. The classification results showed that the model achieved an accuracy of 71%, with stronger performance in identifying negative sentiment compared to positive sentiment. A WordCloud visualization also revealed the frequent appearance of terms such as “mental,” “health,” “self,” and “disorder,” reflecting the main focus of online discussions. These findings indicate that machine learning-based sentiment analysis is effective in capturing public perceptions of mental health issues on social media. This research is expected to contribute to the development of digital communication strategies and real-time monitoring of psychosocial issues in online spaces.
PERANCANGAN SISTEM INFORMASI AKADEMIK BERBASIS WEB PADA MTSN 5 MUARO JAMBI Ramadhan, Fauzan Purma; Dianda Rifaldi; Iriene Putri Mulyadi; Vanji Saputra
Jurnal Rekayasa Perangkat Lunak dan Sistem Informasi Vol. 5 No. 2 (2025)
Publisher : Department of Information System Muhammadiyah University of Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/seis.v5i2.9611

Abstract

The manual management of academic data has the potential to cause various obstacles, such as delays in grade distribution, data duplication, and input errors. This study aims to design a web-based academic information system at MTsN 5 Muaro Jambi as a solution to these problems. The system was developed using the Waterfall method with the PHP programming language and a MySQL database. The database design was carried out in a structured manner to support the integrity and efficiency of academic information management. The novelty of this study lies in the integration of grade management, attendance, and schedule features into a single web-based platform, which has never been implemented before at MTsN 5 Muaro Jambi. The system was designed to involve three main actors: the admin as data manager, teachers as data managers, and students as recipients of online academic information. The implementation results showed that the system was able to improve recording accuracy, accelerate information distribution, and support academic data transparency. Testing using the blackbox testing method proved that the system's main functions ran as needed, while user acceptance trials involving teachers, students, and administrative staff showed the system was easy to use and useful in supporting the academic process.
Co-Authors Abdul Fadlil Abdul Fadlil Abdul Fadlil Alfian Ma’arif Asno Azzawagama Firdaus Bella Okta Sari Miranda Fanani, Galih Fanani, Galih Pramuja Inngam Fauzan Purma Ramadhan Furizal, Furizal Herman Herman, - Iriene Putri Mulyadi Mulyadi, Iriene Putri Murinto Ramadhan, Fauzan purma Saputra, Vanji Tri Stiyo Famuji Tristanti, Novi Vanji Saputra Vanji Saputra6