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All Journal Jurnal Online Mahasiswa (JOM) Bidang Ilmu Ekonomi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Journal of Applied Data Sciences Journal Of Human And Education (JAHE) Jurnal Informatika Ekonomi Bisnis Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK)
Tri Putri Lestari, Tri Putri
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Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences Other

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PENGARUH TEKNOLOGI INFORMASI, PENGENDALIAN INTERN, DAN GAYA KEPEMIMPINAN TERHADAP KINERJA INSTANSI PEMERINTAH (Studi Empiris Pada SKPD Kabupaten Bengkalis) Lestari, Tri Putri; -, Kennedy; Wiguna, Meilda
Jurnal Online Mahasiswa (JOM) Bidang Ilmu Ekonomi Vol 2, No 2 (2015): Wisuda Oktober 2015
Publisher : Jurnal Online Mahasiswa (JOM) Bidang Ilmu Ekonomi

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The purpose of this sstudy is technology information, internal control and leadership style of the performance of government agencies at SKPDs Bengkalis Regency. The research method used is descriptive research method with survey approach, the type of data used iss primary data, primary data by sending questionnaires directly to the respondents and take it back after a predetermined time period. The study population is SKPDs Bengkalis Regency. The sample was echelons III and IV on bodies and agencies in Bengkalis Regency. The classical assumption used is the test for normality, multicollinearity test and test heterokedastisitas. Hypothesis testing using Adjusted R square, Model analysis of the data used I multiple linear regression. Testing the quality of the data used are validity and reliability testing. Simultaneouss significant (Test-f), partial significance (Test-t), the hypothesis testing results show that the hypothesis first, second and third received, this suggests that information technology, internal control and leadership style of the performance of government agencies at SKPDs Bengkalis Regency.Keywords: Information Tecnhology, Internal Control,Leadership style and performance of government agencies.
Analisis Text Mining pada Sosial Media Twitter Menggunakan Metode Support Vector Machine (SVM) dan Social Network Analysis (SNA) Lestari, Tri Putri
Jurnal Informatika Ekonomi Bisnis Vol. 4, No. 3 (September 2022)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (503.663 KB) | DOI: 10.37034/infeb.v4i3.146

Abstract

Online loans are growing rapidly in Indonesia in the last two years. This is because the online loan administration requirements are easier compared to bank financial service loans. Online loans are financial services that provide online-based services. Along with the development of online loans, many illegal online loans have sprung up and often commit violations, such as leaking customer personal information and abusing data by carrying out extreme actions such as terrorizing customers who make online loan transactions. This certainly gets a lot of comments from the public, especially on social media twitter. This study aims to conduct a sentiment analysis to see what phenomena are happening among the public regarding online loans. The data used are tweets or retweets from Twitter social media with #pinjamanonline #pinjol. Twitter social media was chosen because an incident can become a phenomenon if it gets a lot of attention from the community, especially on Twitter social media. In this study, using text mining techniques by applying the Support Vector Machine algorithm to classify sentiments on twitter users regarding online loans. This study also looks at the interactions that occur on social media Twitter using social network analysis (SNA). the results of research and testing of the Support Vector Machine method to classify online loans with an Accuracy value level of 86.6%, with a positive precision of 86%, neutral of 1.00% and negative of 87%, positive recall of 90%, neutral 87% and negative of 26 % and positive F1-Score of 88% neutral 42% and negative 86%. Then at the Social Network Analysis stage there is the most influential account, namely influencer @alvinline21 with 1402 nodes.
Improved Performance of Hybrid GRU-BiLSTM for Detection Emotion on Twitter Dataset Anam, M. Khairul; Munawir, Munawir; Efrizoni, Lusiana; Fadillah, Nurul; Agustin, Wirta; Syahputra, Irwanda; Lestari, Tri Putri; Firdaus, Muhammad Bambang; Lathifah, Lathifah; Sari, Atalya Kurnia
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.459

Abstract

This study addresses emotion detection challenges in tweets, focusing on contextual understanding and class imbalance. A novel hybrid deep learning architecture combining GRU-BiLSTM with SMOTE is proposed to enhance classification performance on an Israel-Palestine conflict dataset. The dataset contains 40,000 tweets labeled with six emotions: anger, disgust, fear, joy, sadness, and surprise. SMOTE effectively balances the dataset, improving model fairness in detecting minority classes. Experimental results show that the GRU-BiLSTM hybrid with an 80:20 data split achieves the highest accuracy of 89%, surpassing BiLSTM alone, which obtained 88%, and other state-of-the-art models. Notably, the proposed model delivers significant improvement in detecting the emotion of joy (recall: 0.87, F1-score: 0.86). In contrast, the surprise category remains challenging (recall: 0.24). Compared to existing research, this study highlights the effectiveness of combining SMOTE and hybrid GRU-BiLSTM, outperforming models such as CNN, GRU, and LSTM on similar datasets. The incorporation of GloVe embeddings enhances contextual word representations, enabling nuanced emotion detection even in sarcastic or ambiguous texts. The novelty lies in addressing class imbalance systematically with SMOTE and leveraging GRU-BiLSTM's complementary strengths, yielding superior performance metrics. This approach contributes to advancing emotion detection tasks, especially in conflict-related social media data, by offering a robust, context-sensitive, and balanced classification method.
Rancang Bangun Aplikasi Menu Interaktif Restoran Berbasis Android Lestari, Tri Putri; Sari, Atalya Kurnia; Ali, Haidar; Kinanti, Dewi Sekar; Larasati, Agnes Indri Pontias; Anjani, Dewi; Anggraeni, Widya Dewi
Semnas Ristek (Seminar Nasional Riset dan Inovasi Teknologi) Vol 9, No 1 (2025): SEMNAS RISTEK 2025
Publisher : Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/semnasristek.v9i1.8079

Abstract

Perkembangan teknologi informasi telah memberikan dampak signifikan dalam berbagai bidang, termasuk industri kuliner. Restoran modern semakin beralih ke solusi digital untuk meningkatkan pengalaman pelanggan dan efisiensi operasional. Penelitian ini bertujuan untuk merancang dan membangun sebuah aplikasi menu interaktif berbasis Android yang memungkinkan pelanggan melihat daftar menu, melakukan pemesanan, dan berinteraksi dengan sistem restoran secara lebih efisien. Metode yang digunakan pada penelitian ini Metode penelitian grounded yang merupakan metode berdasarkan fakta yang bertujuan untuk menetapkan konsep, mengembangkan teori, serta mengumpulkan, menganalisis data pada saat yang bersamaan Hasil dari penelitian ini menunjukkan bahwa aplikasi menu interaktif berbasis Android dapat meningkatkan efisiensi pelayanan, mengurangi kesalahan dalam pemesanan, serta memberikan pengalaman yang lebih modern dan menarik bagi pelanggan. Penggunaan teknologi ini diharapkan dapat membantu restoran dalam meningkatkan kepuasan pelanggan serta mendukung digitalisasi di industri kuliner
Perancangan Model Evaluasi Kinerja untuk Peningkatan Produktivitas Karyawan Restoran Sutrisno, Sutrisno; Lestari, Tri Putri; Maheswari, Denisha Aulia; Quswa, Ahmad Darojatul; Nurhayan, Ilyas; Khairi, Muhammad Qalbi
Semnas Ristek (Seminar Nasional Riset dan Inovasi Teknologi) Vol 9, No 1 (2025): SEMNAS RISTEK 2025
Publisher : Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/semnasristek.v9i1.8081

Abstract

Sumber daya manusia yang berkualitas sangat penting untuk meningkatkan produktivitas kerja suatu organisasi. Kompetensi yang tinggi pada karyawan akan mendukung peningkatan kinerja mereka. Penilaian kinerja diperlukan untuk mengevaluasi prestasi karyawan. Resto Omah Ingkung menghadapi masalah karena belum memiliki format standar untuk penilaian kinerja karyawan. Untuk mengatasi masalah ini, diperlukan sebuah sistem untuk membantu manajer dalam menentukan karyawan dengan kinerja terbaik dan memberikan masukan untuk pengembangan berdasarkan nilai kinerja karyawan. Penelitian ini mengembangkan model evaluasi kinerja karyawan berbasis logika fuzzy. Model ini menggunakan kriteria kehadiran, pelayanan, penampilan, kerjasama, dan tanggung jawab untuk membantu manajer mengidentifikasi karyawan berkinerja terbaik dan memberikan umpan balik yang berguna untuk pengembangan karyawan. Hasil penelitian menunjukkan bahwa model ini memiliki tingkat akurasi 98,19%.
Analyzing the Impact of Artificial Intelligence on Student Learning: A Case Study of SMK Tri Arga 2 Lestari, Tri Putri
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8902

Abstract

The use of artificial intelligence (AI) technology in every aspect of life is a solution that provides an important contribution to the continuity of the wheel of life, not to mention in the world of education. In this digital era, AI has become an important partner and tool for students in completing assignments and assisting in carrying out their learning activities, especially in completing assignments given by teachers. The purpose of this study is to analyze the effect of artificial intelligence on learning, especially for students of SMK Tri Arga 2. The formulation of the research problem involves a description of the use of AI in the process of completing assignments, the benefits and challenges experienced by students, and the most dominant AI applications used in completing these assignments. The research method used is a quantitative descriptive method using a questionnaire, Likert Scale, which is created on Google Form and then sent to the Whatsapp Group and student personal chat. Using a random method. 105 students of SMK Tri Arga 2 participated in this study. the results of this study showed that 51.4% stated that they often use AI. Then 52.4% or 55 students answered that they often use AI in completing school assignments. These results certainly provide a new perspective on the role of AI in helping students complete the school assignments they have been given. It is also hoped that the results of this research can help Senior High Schools (SMA) and Vocational High Schools (SMK) in improving the quality of education and learning by integrating AI more effectively in their students' academic processes, improving supervision and regulations regarding the use of AI in completing assignments and making AI a companion tool while still paying attention to the ethics of plagiarism and the growth of students' skill development.
Implementation of BERTopic for Topic Modeling Analysis of the Free Nutritious Meal Program Based on YouTube Comments Wahyuni, Widya; Lestari, Tri Putri; Apriliana, Milla; Gumelta, Riyang
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9754

Abstract

The Free Nutritious Meal Program (Makan Bergizi Gratis), represents a significant national effort aimed at mitigating stunting rates in Indonesia, having commenced its operations in January 2025. As the program progressed, public sentiment towards it evolved, resulting in a diverse array of opinions that were extensively debated on various social media platforms, notably YouTube. This study was conducted with the objective of examining the perceptions of the public regarding Makan Bergizi Gratis through a topic modeling methodology employing the BERTopic approach, which analyzed 19,843 comments from YouTube. The analytical framework entailed several stages, including data preprocessing, sentence-based embedding representation, dimensionality reduction via UMAP, clustering through HDBSCAN, and topic interpretation grounded in c-TF-IDF. The findings indicate that public commentary is categorizable into ten primary themes, encompassing issues such as the involvement of political figures, concerns over budget transparency, the program's educational benefits, and the need for equitable access in underserved regions. Evaluation results show that BERTopic outperformed the traditional LDA model, with a coherence score of 0.46 compared to 0.39 and topic diversity of 76 percent compared to 71 percent. This analysis reveals that public perception of Makan Bergizi Gratis is multifaceted, shaped by social experience, political context, and economic expectations. These insights may serve as a valuable foundation for a more comprehensive understanding of public opinion, thereby supporting more targeted and responsive policy development.
Sentiment Analysis Optimization Using Ensemble of Multiple SVM Kernel Functions M. Khairul Anam; Lestari, Tri Putri; Efrizoni, Lusiana; Handayani, Nadya Satya; Andhika, Imam
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6708

Abstract

This research aims to optimize sentiment analysis by leveraging the strengths of multiple Support Vector Machine (SVM) kernels—Linear, RBF, Polynomial, and Sigmoid—through an ensemble learning approach. This study introduces a novel model called SVM Porlis, which integrates these kernels using both hard and soft voting strategies to improve the classification performance on imbalanced datasets. Sentiment classification in this study involves two classes: positive and negative. Tweets related to the controversy over the naturalization of Indonesian national football players were collected using the official X/Twitter API, resulting in a dataset of 2,248 entries. The dataset was notably imbalanced, with significantly more negative samples than positive samples. Data preprocessing included cleaning, labeling, tokenization, stopword removal, stemming, and feature extraction using TF-IDF. To address the class imbalance, the SMOTE technique was applied to synthetically augment the minority class. Each SVM kernel was trained and evaluated individually before being combined into an SVM Porlis model. Evaluation metrics included accuracy, precision, recall, F1-score, and confusion matrix analysis. The results demonstrate that SVM Porlis with soft voting achieved the highest performance, with 98% accuracy, precision, recall, and F1-score, surpassing the performance of individual kernels and other ensemble approaches such as SVM + Chi-Square and SVM + PSO. These findings highlight the effectiveness of combining multiple kernels to capture both linear and non-linear patterns, offering a robust and adaptive solution for sentiment analysis in real-world, imbalanced data scenarios.
Pelatihan Peningkatan Kesadaran Brand Awareness Digital Marketing bagi Siswa Pemasaran SMK Budi Warman II Jakarta Irawan, Ines Nur; Lestari, Tri Putri; Lubis, Ratu Balqis Fatya Febrinda; Rahimullah, Muhammad Furqon
Journal Of Human And Education (JAHE) Vol. 4 No. 4 (2024): Journal Of Human And Education (JAHE)
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jh.v4i4.1288

Abstract

Kesadaran merek dalam pemasaran digital sangat penting karena membantu bisnis membangun kehadiran online yang kuat, menjadikan merek mereka lebih dikenal dan mudah diingat oleh konsumen, yang pada akhirnya meningkatkan keterlibatan dan loyalitas pelanggan. Pelatihan ini dirancang dalam pembelajaran yang partifipatif dalam melatih siswa Sekolah Menengah Kejuruan Budi Warman II dalam meningkatkan kesadaran dalam pengetahuan siswa tentang brand awareness tetapi juga mempersiapkan mereka untuk berkontribusi secara efektif dalam industri pemasaran di masa depan. Kegiatan ini dilakukan oleh 130 siswa. Pra-tes dan Pos-tes dilakukan sebagai desk evaluasi. Pelatihan dilaksanakan dalam 2 sesi dengan kurun waktu 3 jam secara luring. Hasil menunjukan bahwa pemahaman dan pengalaman siswa dalam Kesadaran Brand awareness Digital Marketing belum cukup matang dan keterbatasan materi tersebut dalam mata pelajaran pemasaran. Persentase didapati meningkat terhadap dimensi konseptual peserta terhadap digital marketing dalam dunia bisnis setelah dilakukan pelatihan secara sinkronus. Evaluasi dari pelatihan menunjukkan bahwa siswa mengalami peningkatan signifikan dalam pemahaman dan keterampilan mereka terkait pemasaran digital. Hasil ini tidak hanya memperluas pengetahuan siswa tentang brand awareness tetapi juga mempersiapkan mereka untuk berkontribusi secara efektif dalam industri pemasaran di masa depan.
Co-Authors Ali, Haidar Anam, M Khairul Andhika, Imam Anggraeni, Widya Dewi Apriliana, Milla Dewi Anjani, Dewi Efrizoni, Lusiana Firdaus, Muhammad Bambang Gumelta, Riyang Handayani, Nadya Satya Irawan, Ines Nur Irwanda Syahputra Kennedy -, Kennedy Khairi, Muhammad Qalbi Kinanti, Dewi Sekar Larasati, Agnes Indri Pontias Lathifah, Lathifah Lubis, Ratu Balqis Fatya Febrinda Maheswari, Denisha Aulia Meilda Wiguna Munawir Munawir Nurhayan, Ilyas Nurul fadillah, Nurul Quswa, Ahmad Darojatul Rahimullah, Muhammad Furqon Sari, Atalya Kurnia Sutrisno, Sutrisno Widya Wahyuni Wirta Agustin