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All Journal ILKOMNIKA: Journal of Computer Science and Applied Informatics Literatus Innovation in Research of Informatics (INNOVATICS) JOURNAL SCIENTIFIC OF MANDALIKA (JSM) KLIK: Kajian Ilmiah Informatika dan Komputer Journal of Intelligent Software Systems Innovative: Journal Of Social Science Research The Indonesian Journal of Computer Science
Linda Sutriani
Universitas Muhammadiyah Sampit
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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Pengembangan Sistem Informasi BSC Kinerja Redaksi di Radar Sampit Linda Sutriani; Slamet Riyadi
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 1 No. 3 (2020): KLIK Desember
Publisher : STMIK Budi Darma

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

Abstract

Performance is a result achieved by a person in carrying out the tasks assigned to him in terms of quality and quantity in work. To improve the performance of journalists, it is necessary to make efforts so that journalists still have high morale in carrying out their duties. The Balance Scorecard can be used more than just as a measurement system but also to communicate new strategies and align the company to those new strategies. Currently, in the arena of increasingly fierce and competitive business competition, companies are required to sharpen their direction and strategy in an integrated manner so that the company's vision and mission can be realized. However, the process of transforming the company's vision into reality is not easy to implement, it requires a comprehensive performance system that is useful for all elements in the company with the aim of translating the vision and mission into a clear program so that it can be carried out effectively. The Balance Scorecard system is an interesting solution to be applied in an era of continuous transformation, because the system as a whole sees four perspectives, namely: a financial perspective, a customer perspective, an internal business process perspective, and a growth and learning perspective. As a measurement system related to strategy, the four perspectives must present linkages and synergistic relationships as a strategic unit in an effort to achieve long-term goals.
ETL DATA WAREHOUSE ON PERFORMANCE MEASUREMENT Linda Sutriani; Indra Yatini Buryadi; Hera Wasiati; Sudarmanto Sudarmanto
Journal of Intelligent Software Systems Vol 2, No 1 (2023): July
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v2i1.928

Abstract

Measuring the organizational performance correctly will turn out accurate data which can be analyzed to provide important information for management in making decisions to improve company performance. This is because in the past, the performance appraisal was only based on financial measures. However, the editor-in-chief needs more than just financial indicators to improve performance. The Balanced Scorecard measures four organizational dimensions, such as customer, financial, internal business, and learning and growth. Although originally designed for the private sector, many public organizations apply it as modifications to suit their needs. The Balanced Scorecard is an effective method for measuring and achieving organizational performance. This research involves ETL in the Balanced Scorecard process to combine data from various sources into a large central repository called a data warehouse
Kecerdasan Buatan dalam Aspek Deforestasi dan Keberlanjutan Perkebunan: Pendekatan Bibliometrik Sutriani, Linda; Impron, Ali; Saragih, Veny Betsy; Anggraini, Syadza; Suraji, Suraji
LITERATUS Vol 6 No 2 (2024): Jurnal Ilmiah Internasional Sosial Budaya
Publisher : Neolectura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37010/lit.v6i2.1583

Abstract

This study examines the application of Artificial Intelligence (AI) in addressing deforestation and promoting sustainability in plantations using a bibliometric approach. Deforestation, a critical global issue, results from agricultural expansion, plantation development, and land-use changes, leading to significant environmental degradation. AI has been proposed as a powerful tool to monitor and manage deforestation more effectively, offering solutions such as satellite imagery analysis and predictive models. Through a bibliometric analysis spanning the last decade (2013–2023), this study uses VOSviewer to visualize co-citation networks, identifying key research trends and clusters related to AI in deforestation and plantation sustainability. The findings reveal that research is concentrated in regions like Indonesia and Brazil, where AI technologies like machine learning are employed to predict deforestation and enhance resource management. Emerging research areas include the integration of AI with the Internet of Things (IoT) and blockchain for improved data management and sustainability practices. This analysis provides insights into the growing role of AI in mitigating deforestation and offers recommendations for future research, including addressing ethical challenges and regulatory frameworks to further enhance sustainable plantation management.
IoT-Enabed Smart Mining:Pengelolaan Air Limbah di Industri Batubara Impron, Ali; Sutriani , Linda
Innovative: Journal Of Social Science Research Vol. 5 No. 1 (2025): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v5i1.16105

Abstract

Ensuring wastewater quality in coal mining operations is crucial to comply with environmental regulations. One of the key parameters is the pH level, which must be maintained within the range of 6-9. Currently, the monitoring process is manual, involving the use of litmus paper and visual comparison to estimate the pH level. The adjustment of pH is achieved by manually adding limestone until the desired range is met. This research proposes an automated solution leveraging Internet of Things (IoT) technology to streamline both monitoring and control processes. By integrating pH sensors into the water system and employing IoT-enabled relay controls for limestone dispensing, the system enables real-time pH monitoring and automatic limestone release. The proposed system aims to improve the efficiency and accuracy of wastewater treatment processes, aligning with the digitalization efforts and Industry 4.0 standards.
Penerapan Machine Learning Pada Kelapa Sawit: Analisis Bibliometrik Anggraini, Syadza; Saragih, Veny Betsy; Sutriani, Linda
The Indonesian Journal of Computer Science Vol. 13 No. 4 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i4.4224

Abstract

The advancement of machine learning-based technology spread widely, especially in oil palm. Oil palm has become a main source of domestic products because of its high production and a leading commodity where it cannot be separated from the use of machine learning. However, the potential of machine learning has not yet been identified specifically through bibliography aspects where those aspects are needed for future research. The main objective of this research is to analyze trends of machine learning utilization and potential topics in oil palm by using bibliometric analysis to obtain year distribution, author productivity, citation, and keyword co-occurrence. As a result, the highest peak number of publications is 2023 where the most cited authors are Haohuan Fu and Weijia Li. Then, the most used algorithms are deep learning, ANN, SVM, RF and CNN based on the occurrences while the tree detection and counting topic has the highest citation articles. The result indicates that scientific interest in the study of this research benefits as a starting point for future works.
Analisis Sentimen Masyarakat Kalimantan Tengah Terhadap Perkebunan Kelapa Sawit Menggunakan TF-IDF dan Support Vector Machine Putra, Kurniawan Tri; Anggraini, Syadza; Sutriani, Linda; Suraji, Suraji; Impron, Ali
Journal Scientific of Mandalika (JSM) e-ISSN 2745-5955 | p-ISSN 2809-0543 Vol. 6 No. 5 (2025)
Publisher : Institut Penelitian dan Pengembangan Mandalika Indonesia (IP2MI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36312/10.36312/vol6iss5pp1115-1123

Abstract

The transformation towards Society 5.0 has had a significant impact on the rapid growth of data available worldwide, both useful and less directly beneficial, known as big data. This phenomenon provides opportunities for researchers to leverage big data as a valuable source of information, provided it is processed and analyzed using appropriate methods. One of the rapidly growing applications is sentiment analysis, which extracts insights from text data, such as that gathered from social media platforms. This study applies the TF-IDF feature extraction technique and the SVM (Support Vector Machine) classification method to perform sentiment analysis on Twitter text data. The results of the research show that the model built using the combination of TF-IDF and SVM achieved an accuracy of 86%, with precision, recall, and F1-Score values of 85% each. These findings indicate that the application of TF-IDF with SVM provides optimal performance in sentiment analysis, considering the word frequency within documents, and makes a significant contribution to processing big data for more accurate and effective sentiment analysis
Enhancing YOLOv5s with Attention Mechanisms for Object Detection in Complex Backgrounds Environment Impron, Ali; Lestari, Dina; Sutriani, Linda; Anggraini, Syadza; Rizal, Randi
Innovation in Research of Informatics (Innovatics) Vol 7, No 2 (2025): September 2025
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v7i2.16833

Abstract

Enhancing performance for object detection in complex environments is essential for real-world applications that represent complexities, such as stacking objects in the same location or environment. Models for detecting objects developed to this day still have difficulties in detecting objects with environments that have complex backgrounds. The reason is that the model often experiences a decrease in accuracy when the object to be detected is occlusion by other objects and is small in size. Therefore, in this study, a model improvement method was carried out in detecting objects in a complex environment. The algorithm used in this study is YOLOv5s. Optimization is carried out by adding a CBAM (Convolutional Block Attention Module) attention mechanism layer which is integrated with the C3 layer (C3CBAM) in the backbone of the YOLOv5s model architecture. In addition, a P2 feature map is also added to the architecture head. The optimization results carried out were quite satisfactory, namely there was an increase in the precision value by 1.6 %, at mAP@0.5 an increase of 1.4 %, and also mAP@50-95 increased by 0.1%. This proves that the enhancement method applied to YOLOv5s in this study can improve the performance of the model. However, with the addition of the attention mechanism layer, it turns out that it can increase the computational load. Therefore, for future research, a method can be applied to reduce computing load, one of the methods is knowledge distillation.
Peringkasan Hybrid Teks Berita Bahasa Indonesia Berbasis Improved TextRank dan Transformer Anggraini, Syadza; Impron, Ali; Sutriani, Linda; Putra, Kurniawan Tri
ILKOMNIKA Vol 7 No 3 (2025): Volume 7, Number 3, December 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v7i3.806

Abstract

Peringkasan teks otomatis berbahasa Indonesia masih menghadapi tantangan dalam menghasilkan ringkasan yang informatif namun tetap koheren secara semantik. Sebagian besar penelitian sebelumnya hanya menggunakan metode ekstraktif seperti TextRank atau metode abstraktif seperti mT5-small tanpa mengoptimalkan hubungan semantik antar kalimat. Terdapat masalah di antaranya metode ekstraktif cenderung kaku dan tidak mengubah susunan kata dalam kalimat, sedangkan metode abstraktif bisa menyebabkan risiko kesalahan fakta ataupun output yang kurang relevan jika teks terlalu panjang. Untuk mengatasi masalah tersebut tersebut, penelitian ini mengusulkan metode peringkasan teks hybrid yang menggabungkan Improved TextRank dengan mT5-small. Pada tahap awal, dilakukan praproses dan ekstraksi kalimat dengan representasi semantik berbasis embedding. Hasil ekstraksi dimasukkan sebagai input di model mT5-small untuk menghasilkan ringkasan secara abstractive melalui proses parafrasa dan penyusunan ulang kalimat. Penelitian dilakukan terhadap 1000 dokumen berita dataset IndoSum dengan metrik evaluasi ROUGE. Hasil evaluasi menunjukkan bahwa metode usulan mencapai nilai ROUGE sebesar 0.687, 0.451, dan 0.634, melampaui performa TextRank klasik 0.472, 0.307, 0.441 dan mT5-Small 0.553, 0.362, 0.508 untuk hasil evaluasi ROUGE 1, 2 dan L secara berturut-turut. Hasil ini membuktikan bahwa integrasi sentence embedding dan pendekatan hybrid efektif meningkatkan kualitas ringkasan dari segi relevansi semantik. Sehingga pendekatan ini berpotensi menjadi dasar pengembangan model peringkasan teks Bahasa Indonesia yang lebih robust dan semantik.
Co-Authors Dina Lestari, Dina Hera Wasiati Impron, Ali Indra Yatini Buryadi Putra, Kurniawan Tri Rizal, Randi Slamet Riyadi Sudarmanto Sudarmanto suraji suraji Syadza Anggraini Veny Betsy Saragih