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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Indonesian Journal of Artificial Intelligence and Data Mining SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming AS-SABIQUN: Jurnal Pendidikan Islam Anak Usia Dini Teknika Jurnal Riset dan Inovasi Pembelajaran
Lestari, Wulan Sri
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Religion Agriculture, Biological Sciences & Forestry Automotive Engineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Optimization of Sentiment Analysis Classification of ChatGPT on Big Data Twitter in Indonesia using BERT Sinaga, Frans Mikael; Purba, Ronsen; Pipin, Sio Jurnalis; Lestari, Wulan Sri; Winardi, Sunaryo
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7861

Abstract

This research is grounded in the emergence of ChatGPT technology, supported by prior and similar studies. The urgency of the issue is highlighted by previous research indicating non-convergent classification outcomes in LSTM (Long Short-Term Memory) methods due to suboptimal hyperparameter settings and limitations in understanding text data within Big Data. The presence of ChatGPT technology brings both benefits and potential misuse, such as copyright infringement, unauthorized news extraction, and violations of accountability principles. Understanding public sentiment towards the presence of ChatGPT technology is crucial. The research aims to implement the BERT (Bidirectional Encoder Representations from Transformers) method to achieve accurate and convergent sentiment analysis classification. This study involves data preprocessing stages using Natural Language Processing (NLP) techniques. Text data, already vectorized, is classified using BERT to determine public sentiment (positive, negative, neutral) towards ChatGPT technology, ensuring greater accuracy, convergence, and contextual relevance. Performance testing of the BERT model is conducted using a Confusion Matrix. With parameters set to Max Sequence Length = 128 and Batch Size = 16, the highest classification accuracy achieved is 93.4%.
Forecasting Climate Change Patterns to Improving Rice Harvest Using SVR for Achieving Green Economy Juliandy, Carles; Kelvin, Kelvin; Halim, Apriyanto; Pipin, Sio Jurnalis; Sinaga, Frans Mikael; Lestari, Wulan Sri
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 2 (2024): September 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i2.32393

Abstract

The consistently declining rice harvest will cause several economic and environmental problems. The unstable and unpredictable climate change was believed as the main problem of the declining rice harvest. We proposed a method for forecasting climate change to help the farmer in their rice cultivation. We used Support Vector Regression (SVR) to improve algorithm steps such as normalizing the data and applying an Adaptive Linear Combiner (ALC) to optimize the dataset before we processed it with the algorithm. Our model gets 95% accuracy as measured with the confusion matrix. We believe our model will help the farmers in their rice cultivation with good climate forecasting. A further benefit of this research we belief that with the well-forecasted climate, the usage of pesticides will decrease and will help the vision of the Indonesian government with a green economy
Comparison of Deep Neural Networks and Random Forest Algorithms for Multiclass Stunting Prediction in Toddlers Lestari, Wulan Sri; Saragih, Yuni Marlina; Caroline
Teknika Vol. 13 No. 3 (2024): November 2024
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v13i3.1063

Abstract

Stunting in toddlers is a serious global health issue, with long-term impacts on physical growth and cognitive development. To address this problem more effectively, it is crucial not only to identify whether a child is stunted but also to predict the severity of the condition. Multiclass stunting prediction offers deeper insights into a child’s condition, enabling more precise and targeted interventions. This study aims to compare the performance of multiclass stunting prediction models using two machine learning algorithms: Deep Neural Networks and Random Forest. The research process involved data collection, preprocessing, as well as model development and testing. The results show that the Random Forest model achieved 100% accuracy in training and 99.92% accuracy in testing, while the Deep Neural Networks model achieved 93.49% accuracy in training and 93.21% in testing. Both models demonstrated strong performance in multiclass stunting prediction, with Random Forest proving superior in terms of accuracy compared to Deep Neural Networks.
Pengembangan Aplikasi Area untuk Meningkatkan Pemahaman Matematis Siswa pada Materi Keliling Bangun Datar Kelas V Lestari, Wulan Sri; Irawati, Riana; Maulana, Maulana
AS-SABIQUN Vol 7 No 1 (2025): JANUARI
Publisher : Pendidikan Islam Anak Usia Dini STIT Palapa Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36088/assabiqun.v7i1.5566

Abstract

This investigation was prompted by pupils' inability to comprehend the diameter of two-dimensional figures. Students often experience boredom in studying due to the teacher's exclusive reliance on lecture methods, which adversely impacts their comprehension of the circumference of planar shapes. This research aims to create an Android-based application named AREA to enhance fifth-grade students' comprehension of flat figures at SD Negeri Setiajaya 01 and to evaluate its practicality. The employed research methodology is Research and Development (R&D) utilizing the ADDIE model for the development phase. The research instruments employed included validation sheets for material experts, media experts, pretest-posttest questionnaires, and response surveys for teachers and students. Data analysis methodologies for expert and educator validation are assessed via a Likert scale; thereafter, pretest and posttest evaluations employ N-Gain formula analysis, while media appropriateness is gauged by questionnaires directed at instructors and students. The research findings indicate that the Android-based AREA media is highly valid for usage, as evidenced by validation results from material and media specialists, which yielded an average percentage of 98% in the highly viable category. The media's suitability can be assessed through the teacher questionnaire results, which yielded a score of 92%, and the student response questionnaire, which indicated a score of 99%. According to these findings, AREA media can be classified as highly appropriate for educational purposes. Apart from that, the N-gain persent or N-gain percentage results were also abtained at 66,93% so that based on these result it can be said that AREA media is “effective enough” to be used in the learning process.
Optimization of Sentiment Analysis Classification of ChatGPT on Big Data Twitter in Indonesia using BERT Sinaga, Frans Mikael; Purba, Ronsen; Pipin, Sio Jurnalis; Lestari, Wulan Sri; Winardi, Sunaryo
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7861

Abstract

This research is grounded in the emergence of ChatGPT technology, supported by prior and similar studies. The urgency of the issue is highlighted by previous research indicating non-convergent classification outcomes in LSTM (Long Short-Term Memory) methods due to suboptimal hyperparameter settings and limitations in understanding text data within Big Data. The presence of ChatGPT technology brings both benefits and potential misuse, such as copyright infringement, unauthorized news extraction, and violations of accountability principles. Understanding public sentiment towards the presence of ChatGPT technology is crucial. The research aims to implement the BERT (Bidirectional Encoder Representations from Transformers) method to achieve accurate and convergent sentiment analysis classification. This study involves data preprocessing stages using Natural Language Processing (NLP) techniques. Text data, already vectorized, is classified using BERT to determine public sentiment (positive, negative, neutral) towards ChatGPT technology, ensuring greater accuracy, convergence, and contextual relevance. Performance testing of the BERT model is conducted using a Confusion Matrix. With parameters set to Max Sequence Length = 128 and Batch Size = 16, the highest classification accuracy achieved is 93.4%.
Innovarte Learning: Media Pembelajaran Berbasis Augmented Reality Bagi Mahasiswa Penyandang Disabilitas Lestari, Wulan Sri; Ulina, Mustika; Gunawan, Gunawan; Gaol, Manto Lumban
Jurnal Riset dan Inovasi Pembelajaran Vol. 4 No. 3 (2024): September-December 2024
Publisher : Education and Talent Development Center Indonesia (ETDC Indonesia)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51574/jrip.v4i3.2426

Abstract

Kesenjangan akses pendidikan bagi mahasiswa penyandang disabilitas masih menjadi tantangan serius. Penelitian ini bertujuan untuk mengembangkan dan mengevaluasi efektivitas modul pembelajaran interaktif berbasis Augmented Reality (AR) yang selanjutnya disebut dengan InnovARte Learning untuk meningkatkan pemahaman dan keterlibatan mahasiswa, khususnya mahasiswa penyandang disabilitas. Proses analisis dilakukan melalui serangkaian tahapan, dimulai dengan analisis kebutuhan menggunakan Focus Group Discussion (FGD) Bersama psikolog, yang memberikan wawasan untuk desain modul. Modul yang dikembangkan mengintegrasikan teknologi AR, video pembelajaran, dan kuis berbasis permainan. Selanjutnya, implementasi modul dilakukan, disertai evaluasi melalui metode pra-eksperimen, yang melibatkan pengukuran dengan pretest, posttest, ujian, dan kuesioner. Penelitian ini dilakukan di Program Studi Teknologi Informasi Universitas Mikroskil dengan melibatkan mahasiswa reguler serta mahasiswa penyandang disabilitas, termasuk Autism Spectrum Disorder, Disabilitas Intelektual, dan Kesulitan Belajar. Hasil analisis data menunjukkan adanya peningkatan rata-rata nilai posttest dibandingkan pretest, meskipun mahasiswa dengan Disabilitas Intelektual memerlukan pendekatan tambahan untuk memahami materi. Data kuesioner menunjukkan 64,87% responden merasa modul ini efektif, mudah digunakan, dan mendukung pembelajaran fleksibel, sementara 35,12% bersikap netral. Hasil evaluasi ini menunjukkan bahwa modul InnovARte Learning merupakan inovasi yang relevan dalam mendukung pendidikan inklusif, menyediakan akses pembelajaran yang lebih mudah dan menyenangkan bagi mahasiswa penyandang disabilitas.
Peningkatan pemanfaatan sistem automasi perkantoran pada perkumpulan pemuda Theravada Indonesia Paulus, Paulus; Hanes, Hanes; Lestari, Wulan Sri; William, William
SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Vol 9, No 4 (2025): Juli
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jpmb.v9i4.31982

Abstract

AbstrakPemuda Theravada Indonesia adalah perkumpulan sosial keagamaan berskala nasional dengan kepengurusan  yang tersebar di 23 provinsi di seluruh Indonesia. Organisasi ini telah memanfaatkan sistem automasi perkantoran berupa Microsoft 365 dan Google Workspace untuk organisasi nirlaba. Akan tetapi, tingkat pemanfaatan kedua sistem tersebut masih sangat rendah. Kegiatan Pengabdian pada Masyarakat ini bertujuan untuk mengeksplorasi dan mengimplementasikan solusi agar sistem automasi perkantoran dapat dimanfaatkan lebih efektif untuk mendukung kegiatan Perkumpulan. Tahapan yang dilakukan tim pelaksana mencakup analisis kondisi, merumuskan solusi, implementasi sistem, dan memberikan dukungan / pendampingan. Hasil wawancara, diskusi, observasi sistem, dan studi dokumen Perkumpulan memperlihatkan bahwa rendahnya pemanfaatan sistem automasi perkantoran disebabkan beberapa kendala. Kendala yang dimaksud yaitu belum ada pengurus Perkumpulan yang bertugas sebagai pengelola sistem / teknologi, kurangnya pelatihan sistem dan sosialisasi, dan belum ada program kerja sehubungan peningkatan sistem automasi perkantoran. Tim Pengabdian kepada Masyarakat membantu merumuskan solusi agar Perkumpulan membuat kebijakan internal tentang pemanfaatan sistem automasi perkantoran. Berdasarkan kebijakan tersebut, tim pelaksana melakukan implementasi dan kemudian memberikan dukungan / pendampingan selama implementasi. Pendampingan dilakukan dalam bentuk sosialisasi dan pelatihan yang diikuti oleh 90 anggota Perkumpulan dan dilakukan secara daring. Upaya ini membuahkan dampak positif terhadap efisiensi kegiatan, kolaborasi dan pengelolaan data Perkumpulan. Kata kunci: organisasi nirlaba; digitalisasi; automasi perkantoran; efisiensi proses; Microsoft 365. AbstractPemuda Theravada Indonesia, a national socio-religious association with branches in 23 provinces across Indonesia. This organization has utilized office automation systems such as Microsoft 365 and Google Workspace for nonprofit organizations. However, the level of utilization of both systems is still very low. This Community Service aims to explore and implement solutions to enhance the effective use of office automation systems in the Association’s activities. The Community Service Team conducted condition analysis, formulated solutions, implemented the systems, and provided support / assistance. Interviews, discussions, system observations, and document studies revealed that the low utilization of office automation systems is due to several obstacles. These obstacles include the absence of system / technology managers, lack of system training and socialization, and the absence of work programs related to the enhancement of office automation systems. The Community Service Team assisted in formulating solutions for the Association to establish internal policies on the utilization of office automation systems. Based on these policies, the implementation team carried out the implementation and subsequently provided support and assistance for the Association during the implementation. Mentoring was carried out in the form of socialization and training attended by 90 members of the Association and was carried out online. This effort has had a positive impact on the efficiency of activities, collaboration and data management of the Association. Keywords: nonprofit organization; digitalization; office automation; process efficiency; Microsoft 365.
Pelatihan Pemanfaatan Microsoft Teams Untuk Mendukung Perkuliahan Online Siagian, Hanny; Lestari, Wulan Sri; Saragih, Yuni Marlina
Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Vol 6, No 2 (2023): Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstormin
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/japhb.v6i2.3542

Abstract

Covid-19 yang telah melanda dunia termasuk Indonesia, memberikan dampak yang terlihat nyata dalam berbagai bidang, salah satunya yaitu dalam bidang pendidikan.  Pelaksanaan pendidikan di Indonesia dalam masa pandemi Covid-19 mengalami perubahan, dimana masyarakat dituntut untuk melaksanakan pembelajaran secara online (tanpa tatap muka secara langsung) atau istilah lainnya yaitu daring (dalam jaringan/sistem pembelajaran jarak jauh). Salah satu sarana pembelajaran daring yang dapat dimanfaatkan yaitu Microsoft Teams. Namun belum semua peserta didik memahami cara penggunaan dari Microsoft Teams tersebut, sehingga dibutuhkan pelatihan pemanfaatan Microsoft Teams tersebut. Pelatihan ini berlangsung melalui video conference di Microsoft Teams bersama 40 orang mahasiswa baru. Melalui pelatihan ini diharapkan mahasiswa baru dapat mengenal dan memahami cara pembelajaran online di Mikroskil dan mengetahui cara penggunaan Microsoft Teams supaya dapat mendukung perkuliahan online nantinya. Sebelum kegiatan pelatihan, dilakukan kegiatan pre-test dan di akhir pelatihan dilakukan kegiatan post-test untuk mengukur pemahaman mahasiswa/i baru Mikroskil sebelum dan sesudah mengikuti pelatihan. Hasil penilaian pre-test memperoleh nilai rata-rata 16,91 sedangkan nilai rata-rata post-test 20,06. Hal ini menunjukkan bahwa mahasiswa/i baru memiliki gambaran mengenai pelaksanaan perkuliahan online dan mampu menggunakan tools perkuliahan online yaitu Microsoft Teams.
Co-Authors Apriyanto Halim Carles Juliandy Caroline Gaol, Manto Lumban Gunawan Gunawan Hanes Hanes, Hanes Hanny Siagian Irawati, Riana Kelvin Kelvin Maulana Maulana, Maulana Paulus Paulus Pipin, Sio Jurnalis Ronsen Purba Saragih, Yuni Marlina Sinaga, Frans Mikael Ulina, Mustika William William Winardi, Sunaryo