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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Public Health Science (IJPHS) Jurnal Ilmu Komputer dan Informasi Berkala Ilmiah Pendidikan Biologi (BioEdu) Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) International Journal of Advances in Intelligent Informatics Jurnal Kimia dan Kemasan Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JPM (Jurnal Pemberdayaan Masyarakat) Communications in Science and Technology SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan JPF : JURNAL PENDIDIKAN FISIKA Jurnal Ilmu Fisika Jurnal Informatika Global Jurnal Abdimas Mandiri Jurnal Ilmiah Matematika dan Pendidikan Matematika (JMP) Indonesian Journal of Electrical Engineering and Computer Science Abdimas Galuh: Jurnal Pengabdian Kepada Masyarakat Reswara: Jurnal Pengabdian Kepada Masyarakat Indonesian Journal of Engagement, Community Services, Empowerment and Development (IJECSED) Malcom: Indonesian Journal of Machine Learning and Computer Science Prosiding Seminar Nasional Official Statistics Hexatech: Jurnal Ilmiah Teknik Jurnal Indonesia Sosial Teknologi Eduvest - Journal of Universal Studies Molekul: Jurnal Ilmiah Kimia Al-Ashri: Ilmu-Ilmu Keislaman AQILA : Acceleration, Quantum, Information Technology and Algorithm Journal
Evi Yulianti
Pusat Teknologi Bahan Industri Nuklit-BATAN Kawasan PUSPIPTEK Serpong, Tangerang 15314, Indonesia
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Payroll Checker System Based on Employee Performance at PT GOTO Palembang Using FDD Marcelina, Dona; Putri, Indah Pratiwi; Yulianti, Evi
Acceleration, Quantum, Information Technology and Algorithm Journal Vol. 2 No. 1 (2025): VOLUME 2, NO 1: JUNE 2025
Publisher : Yayasan Asmin Intelektual Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62123/aqila.v2i1.78

Abstract

This study addresses the critical need for modernising payroll management at PT GOTO Palembang, a rapidly expanding enterprise grappling with the limitations of manual, paper-based salary processing. The research focuses on the design and implementation of a web-based Payroll Checker Information System that integrates employee performance metrics to enhance accuracy, efficiency, and transparency in remuneration processes. Employing the Feature-Driven Development (FDD) methodology, the system was developed iteratively to ensure alignment with organisational requirements and adaptability to evolving operational complexities. The system architecture encompasses modules for attendance tracking, performance evaluation, salary computation—including deductions, allowances, and meal subsidies—and comprehensive reporting functionalities. Role-based access controls were instituted to safeguard data integrity and facilitate secure access for administrators, employees, and directors. The implementation of this system has yielded significant improvements in payroll accuracy, reduced administrative workload, and enhanced employee trust through increased transparency. Furthermore, the integration of performance-based compensation aligns employee incentives with organisational objectives, fostering a culture of accountability and continuous improvement. This initiative exemplifies how strategic application of information technology can optimise human resource management practices, particularly in organisations experiencing rapid growth and structural complexity. The findings underscore the efficacy of agile development methodologies in delivering scalable and responsive business solutions, and they offer valuable insights for enterprises seeking to modernise their payroll systems in alignment with contemporary performance management paradigms.
A Prognostic System for Pharmaceutical Inventory Forecasting Using the Trend Least Squares Method at Rakha Medika Yulianti, Evi; Putri, Indah Pratiwi; Marcelina, Dona
Acceleration, Quantum, Information Technology and Algorithm Journal Vol. 2 No. 1 (2025): VOLUME 2, NO 1: JUNE 2025
Publisher : Yayasan Asmin Intelektual Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62123/aqila.v2i1.80

Abstract

This study proposes the development of a sophisticated predictive system for pharmaceutical inventory management at Rakha Medika, Palembang, aimed at addressing the prevalent challenges associated with inaccurate drug stock forecasting. Employing the Trend Least Squares method, the system leverages historical consumption data to generate precise predictions of future pharmaceutical needs, thereby facilitating optimal procurement strategies and mitigating the risks of both stockouts and surplus inventory. Developed with PHP and MySQL, the system offers a user-friendly web-based interface, providing role-specific access for administrators, warehouse personnel, and senior management, ensuring seamless integration within the existing operational framework. This research highlights the importance of data-driven decision-making in healthcare supply chain management, where the accuracy of stock forecasts directly correlates with the quality-of-service delivery. Through rigorous testing using real-world data, the system demonstrated a significant improvement in forecasting accuracy and operational efficiency, with tangible benefits including reduced administrative burdens and enhanced drug availability. The implementation of this predictive system not only optimizes inventory control but also contributes to the overall enhancement of healthcare services at the public health center.
Academic expert finding using BERT pre-trained language model Mannix, Ilma Alpha; Yulianti, Evi
International Journal of Advances in Intelligent Informatics Vol 10, No 2 (2024): May 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v10i2.1497

Abstract

Academic expert finding has numerous advantages, such as: finding paper-reviewers, research collaboration, enhancing knowledge transfer, etc. Especially, for research collaboration, researchers tend to seek collaborators who share similar backgrounds or with the same native languages. Despite its importance, academic expert findings remain relatively unexplored within the context of Indonesian language. Recent studies have primarily relied on static word embedding techniques such as Word2Vec to match documents with relevant expertise areas. However, Word2Vec is unable to capture the varying meanings of words in different contexts. To address this research gap, this study employs Bidirectional Encoder Representations from Transformers (BERT), a state-of-the-art contextual embedding model. This paper aims to examine the effectiveness of BERT on the task of academic expert finding. The proposed model in this research consists of three variations of BERT, namely IndoBERT (Indonesian BERT), mBERT (Multilingual BERT), and SciBERT (Scientific BERT), which will be compared to a static embedding model using Word2Vec. Two approaches were employed to rank experts using the BERT variations: feature-based and fine-tuning. We found that the IndoBERT model outperforms the baseline by 6–9% when utilizing the feature-based approach and shows an improvement of 10–18% with the fine-tuning approach. Our results proved that the fine-tuning approach performs better than the feature-based approach, with an improvement of 1–5%.  It concludes by using IndoBERT, this research has shown an improved effectiveness in the academic expert finding within the context of Indonesian language.
The Synthesized-Hydroxyapatite Powder from Anadara Granosa Shells using Deposition Time Method for Biomedical Applications Sunardi, Sunardi; A’yun, Nidha Aulia Qurrata; Dari, Qorinah Wulan; Aminuddin, Jamrud; Bilalodin, Bilalodin; Praktino, Budi; Yulianti, Evi; Utomo, Agung Bambang Setio; Sari, Kartika
Jurnal Ilmu Fisika Vol 16 No 1 (2024): March 2024
Publisher : Jurusan Fisika FMIPA Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jif.16.1.88-96.2024

Abstract

Hydroxyapatite (HAp) powder, one of the biomaterials derived from natural sources, could be used in biomedical applications. In this research, the synthesized-HAp powder from Anadara Granosa shells as raw materials had a high calcium carbonate content with variations in deposition time using the precipitation method. Variations of deposition time used were 0 (S0), 24 (S24), and 48 (S48) hours. Fourier Transform Infrared (FTIR), X-Ray Diffractions (XRD), and Scanning Electron Microscopy (SEM) were used to investigate the chemical structure, phase analysis, and morphology of the synthesized HAp powder. FTIR results of the S0, S24, and S48 showed that the functional groups ,  and were formed at variations in the deposition time. The XRD results showed that the smallest of crystallite size of S48 was 26.03 nm, and the crystallinity degree of S24 was 38.74%. The grain dispersity of the synthesized-hydroxyapatite powder from SEM results were uniform, agglomeration, and spherical, irregular shape. The Ca, P, Mg, and Si compositions were shown in the synthesized-hydroxyapatite powder. The deposition time affects the synthesized-Hydroxyapatite (HAp) powder from the Anadara Granosa shell, and it is a potential raw material for biomedical applications.
Sentiment Analysis of Tweets Before the 2024 Elections in Indonesia Using Bert Language Models Geni, Lenggo; Yulianti, Evi; Sensuse, Dana Indra
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26490

Abstract

General election is one of the crucial moments for a democratic country, e.g., Indonesia. Good election preparation can increase people's participation in the general election. In this study, we conduct a sentiment analysis of Indonesian public opinion on the upcoming 2024 election using Twitter data and IndoBERT model. This study is aimed at helping the government and related institutions to understand public perception. Therefore, they could obtain valuable insights to better prepare for elections, including evaluating the election policies, developing campaign strategies, increasing voter engagement, addressing issues and conflicts, and increasing transparency and public trust. The main contribution of this study is threefold: (i) the application of state-of-the-art transformer-based model IndoBERT for sentiment analysis on political domain; (ii) the empirical evaluation of IndoBERT model against machine learning and lexicon-based models; and (iii) the new dataset creation for sentiment analysis in political domain. Our Twitter data shows that Indonesian public mostly reacts neutrally (83.7%) towards the upcoming 2024 election. Then, the experimental results demonstrate that IndoBERT large-p1 is the best-performing model that achieves an accuracy of 83.5%. It improves our baseline systems by 48.5% and 46.49% for TextBlob, 2.5% and 14.49% for Multinomial Naïve Bayes, and 3.5% and 13.49% for Support Vector Machine in terms of accuracy and F-1 score, respectively.
From Text to Truth: Leveraging IndoBERT and Machine Learning Models for Hoax Detection in Indonesian News Ridho, Muhammad Yusuf; Yulianti, Evi
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 3 (2024): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i3.29450

Abstract

In the era of technology and information exchange online content being deceitful poses a serious threat to public trust and social harmony on a global scale. Detective mechanisms to identify content are essential for safeguard the populace effectively. This study is dedicated to creating a machine learning system that can automatically spot deceptive content in Indonesian language by utilizing IndoBERT. A model specifically tailored for the intricacies of the Indonesian language. IndoBERT was selected due to its capacity to grasp the linguistic nuances present, in Indonesian text which are often challenging for other models built upon the BERT framework. The key focus of this study lies in conducting an assessment of the IndoBERT model in relation to other approaches used in past research for identifying fake news like CNN LSTM and various classification models such as Logistic Regression and Naïve Bayes among others. To address the issue of imbalanced data between valid labels in fake news detection tasks we employed the SMOTE oversampling technique, for data augmentation and balancing purposes. The dataset employed consists of Indonesian language news articles publicly available and categorized as either hoax or valid following assessment by three judges voting system. IndoBERT Large demonstrated performance by achieving an accuracy rate of 98% outperform the original datasets 92% when tested on the oversampled dataset. Utilizing the SMOTE oversampling technique aided in data balance and enhancing the models performance. These outcomes highlight IndoBERTs capabilities in detecting fake news and pave the way for its potential integration, into real world scenarios.
WORKSHOP PENDAMPINGAN PENGGUNAAN APLIKASI E-VOTING UNTUK PEMILIHAN OSIS Saluza, Imelda; Yulianti, Evi; Astuti, Lastri Widya; Dhamayanti, Dhamayanti
Reswara: Jurnal Pengabdian Kepada Masyarakat Vol 5, No 1 (2024)
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/rjpkm.v5i1.4047

Abstract

Salah satu kegiatan non akademik di sekolah kegiatan Organisasi Siswa Intra Sekolah (OSIS). SMP Negeri 4 Banyuasin 1 Sumatera Selatan selaku mitra tim PKM menyampaikan permasalahan pada kegiatan non akademik yang dihadapi berdasarkan hasil observasi dan wawancara. Dalam proses pelaksanaan kegiatan OSIS, ditemui beberapa hambatan antara lain belum adanya pemanfataan teknologi dalam kegiatan non akademik mitra padahal mitra telah memiliki kesiapan teknologi serta sumber daya untuk menggunakannya, kegiatan non akademik yang masih menganggu proses pembelajaran serta tidak efektif dalam kegiatannya. Berdasarkan permasalahan yang dihadapi mitra, Pengabdian Kepada Masyarakat (PKM) Universitas Indo Global Mandiri (IGM) dan mitra menyepakati untuk mengadakan kegiatan PKM penggunaan aplikasi e-voting guna mendukung bidang non akademik. Tujuan kegiatan ini adalah untuk memberikan bimbingan teknis yang menjelaskan bagaimana sistem e-voting bekerja, termasuk bagaimana suara dihitung dan hasil diumumkan. Hal ini dapat meningkatkan transparansi dan kepercayaan para pemilih terhadap integritas pemilihan. Kegiatan ini dapat memberikan wawasan tentang bagaimana pemilihan berlangsung yang memungkinkan panitia pemilihan OSIS untuk memantau, mengevaluasi proses serta membuat perbaikan di masa akan datang serta membantu kebutuhan dalam pelaksanaan pembaruan struktur ogansisasi mitra dalam menunjang pelaksanaan kegiatan non akademik. Kegiatan dilakukan dengan menggunakan metode difusi IPTEKS dan workshop. Workshop dilaksanakan tanggal 19 April 2023 untuk melakukan evaluasi terhadap kegiatan tim memberikan kuesioner kepada peserta untuk menilai kegiatan yang telah dilakukan. Berdasarkan analisis hasil evaluasi kegiatan PKM mitra menilai pelaksanaan workshop memberikan kemudahan dalam pemilihan dibanding acara tradisional, proses perhitungan lebih cepat sehingga efektif digunakan, perhitungan, keamanan dan kerahasiaan terjamin. Hasil penilaian dari peserta dapat disimpulkan bahwa peserta menyetujui untuk menggunakan aplikasi e-voting untuk pemilihan OSIS dengan persentase indeks penilaian di atas 75%.
From Watching To Purchasing: The Influence Of It Affordance Dimensions On Live Streaming Marketplace Yulianti, Evi; Hasnawati, Hasnawati
Jurnal Indonesia Sosial Teknologi Vol. 5 No. 11 (2024): Jurnal Indonesia Sosial Teknologi
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jist.v5i11.1751

Abstract

Live streaming marketplaces are currently growing rapidly and make it easier for various parties to interact, shop, and promote products in real-time. This study examines the influence of factors in IT Affordance (Visibility, Metavocing, Guidance Shopping, Trading Affordance, Triggered Attending, and Interactivity) on Immersion directly and on Actual Purchase indirectly on live streaming media on the marketplace. This research data was obtained using a questionnaire in the form of a google form and disseminated through WhatsApp and other social media networks such as Instagram and Twitter or X and as many as 183 google forms have been filled. All answers are eligible for processing. The results of this study show that Metavocing, Trading Affordance, Triggered Attending, and Interactivity have a positive effect on Immersion, while Visibility and Guidance shopping have no effect on Immersion, and Immersion has a positive effect on Immersion. Actual Purchase. However, all variables in IT Affordance (Visibility, Metavocing, Guidance shopping, Trading Affordance, Triggered Attending, and Interactivity) have no indirect effect on  the Actual Purchase mediated by Immersion
Pelatihan pemanfaatan teknologi artificial intelligence bagi guru sekolah dasar Saluza, Imelda; Yulianti, Evi; Putri, Indah Pratiwi; Marcelina, Dona; Sartika, Dewi
SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Vol 8, No 2 (2024): June
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jpmb.v8i2.23838

Abstract

Abstrak Dalam melaksanakan proses pembelajaran, guru dituntut untuk dapat terus menerus melakukan pembelajaran yang adaptif dan up to date yang didukung teknologi canggih. Teknologi canggih yang sering digunakan adalah Artificial Intellegence. Berdasarkan hasil observasi dan diskusi tim pengabdian kepada masyarakat (PKM) dan kepala sekolah SD Negeri 13 Palembang belum pernah mendapatkan pengetahuan memanfaatkan AI untuk mendukung proses pembelajaran dalam hal pencarian materi dan bahan ajar, karenanya tim PKM dan mitra memutuskan untuk melakukan kegiatan pelatihan memanfaatkan AI dalam pembelajaran. Dalam pelaksanaannya dilakukan dengan menggunakan metode Participatory Action Research (PAR) dengan empat tahapan yaitu perencanaan, tindakan, evaluasi dan refleksi. Setelah kegiatan dilaksanakan dilakukan analisis dari proses evaluasi dan refleksi kegiatan. Hasil evaluasi menunjukkan rata-rata peserta pelatihan mengalami peningkatan pengetahuan dan keterampilan sebesar 68,9%. Sedangkan hasil refleksi menjelaskan faktor pendukung dan kendala pelaksanaan. Adapun faktor pendukung yaitu adanya antusias peserta, dukungan kepala sekolah dan layanan internet yang lancar. Sedangkan faktor kendala antara lain adalah terdapat guru yang tidak membawa laptop, ada guru yang hampir pensiun dan merasa kurang membutuhkan pelatihan serta guru yang lupa akun email. Kata kunci: pembelajaran; materi; bahan ajar; metode PAR. Abstract In carrying out the learning process, teachers are required to be able to continuously carry out adaptive and up-to-date learning supported by advanced technology. The advanced technology that is often used is Artificial Intelligence. The community service team (PKM) and the principal of SD Negeri 13 Palembang conducted observations and had discussions. Based on their findings, they decided to conduct training activities that use AI in learning because they had no prior experience using it to support the learning process in terms of finding resources and teaching materials. Planning, action, evaluation, and reflection are the four stages of the Participatory Action Research (PAR) methods that were used in its execution. Following the completion of the task, an analysis of the assessment procedure and a contemplation of the task are conducted. According to the evaluation data, the typical training participant saw a 68.9% improvement in knowledge and abilities. In the meantime, the reflection's findings clarify the implementation's enabling elements and challenges. Enthusiastic participation, the principal's encouragement, and reliable internet access are the supporting aspects. In the meanwhile, teachers who forget their email accounts, are nearing retirement and believe they don't need training, and don't bring laptops are all restrictive factors. Keywords: learning; materials; teaching materials; PAR methods.
Sistem Pendukung Keputusan Penentuan Mutu Tanaman Karet (Studi Kasus PT. Hevea MK 1) Yulianti, Evi; Marcelina, Dona; Aulia, Muti’a Rahma
Jurnal Ilmiah Informatika Global Vol. 14 No. 1
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jiig.v14i1.2976

Abstract

Determination of Product Quality of Hevea Brasiliensis (Rubber) at PT. Hevea MK 1 Palembang is very important in improving the quality of service to consumers. PT. Hevea MK 1 Palembang has not maximally utilized Information System Technology in determining the best quality of latex, resulting in a relatively long time in calculating the quality value of latex rubber, the need for Information Systems Technology that can support Decision Making to solve structured and unstructured problems. Based on the problems above, we need a system that can solve the quality problem of Hevea brasiliensis (rubber) sap, with the Decision Support System determining the quality of Hevea Brasiliensis (rubber) sap is expected to help and facilitate the company in choosing the type of superior rubber seeds. This study aims to determine the best quality of latex Hevea Brasiliensis rubber at PT. Hevea MK 1 Palembang uses the promethee method. The output of this application is in the form of an alternative decision with the highestranking value. The Decision Support System was developed using PHP and MySQL programming as a Database Management System (DBMS). System testing resulted in Rank 1, namely A2 with Leaving Flow = 0.583333, Entering Flow = 0, Net Flow = 0.583333 while Rank 2, namely A3 with Leaving Flow = 0.166667, Entering Flow = -0.25, Net Flow = -0 .8333 and Rank 3, namely A1 with Leaving Flow = 0, Entering Flow = 0.5, Net Flow = -0.5
Co-Authors Abdul Haris Abdurrohman, Jafar Abro, Fikri Adriana, Risda Agung Bambang Setio Utomo Agus Sugandha Ahmad Dahlan Alfina, Ika Alie, M. Fadhiel Aminuddin, Jamrud Anandez, Arum Adisha Putra Annas, Dicky Atmoko, Indri Aulia, Muti’a Rahma A’yun, Nidha Aulia Qurrata Bambang Subeno Berghuis, Nila Tanyela Bhary, Naradhipa Bilalodin Bilalodin Budi, Indra Busral, Busral Coyanda, John Roni Cyndika Dana Indra Sensuse Dari, Qorinah Wulan DEWI SARTIKA Dhamayanti, Dhamayanti Dwitilas, Fariz Wahyuzan Eka Qadri Nuranti Enrique, Gabriel Faradillah Fatari, Fatari Febrianto, Muhamad Rizki Fridarima, Shanny Geni, Lenggo Gupron, Akhmad Hananto, Djoko Haryadi, Arifin Nur Muhammad Hasnawati Hasnawati Hayati, Atika Trisna Heri Jodi, Heri Humairoh, Nayu Nur Husin, Husna Sarirah Imelda Saluza, Imelda Indah Permatasari Iskandar Zulkarnaen Jayawarsa, A.A. Ketut Kartika Sari Khusaenah, Nur Kurniawan, Alfin Lastri Widya Astuti, Lastri Widya Laugiwa, Matiin Lukman Hakim Madiabu, Muhammad Jihad Mannix, Ilma Alpha Marcelina, Dona Martawijaya, M. Agus Meganingrum Arista Jiwanggi Ndruru, Sun Theo Constan Lotebulo Nissa, Nuzulul Khairu Nua, Muh. Tri Prasetia Pisgamargareta, Abel Praktino, Budi Prasetyo, Ridho Pratama, Mochamad Jodi Pratiwi, Indah Putri Putri Rizqiyah Putri, Indah Pratiwi Rabiyatul Adawiyah Siregar Rachmadhanti, Elvira Nur Rachmawati, Nur Rama Samudra, M.S Ramadhan, Mustafa Ridho, Muhammad Yusuf Rohmad Salam, Rohmad Rosiana Dwi Saputri, Rosiana Dwi Sampora, Yulianti Saputra, Muklas Ade Sofyan, Muhammad Ihsan Sudaryanto Sulkiah Hendrawati Sumarsih, Rani Sri Sunardi Sunardi Suryati Syazali, Muhammad Rizki Terttiaavini Terttiavini, Terttiavini Zulham Zulham