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Decision Support System For Selection of Prospective Members of BLM Polytechnic Caltex Riau Using The Weighted Product Method Vandi Rahman; Dini Hidayatul Qudsi
TIERS Information Technology Journal Vol. 4 No. 1 (2023)
Publisher : Universitas Pendidikan Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38043/tiers.v4i1.4340

The Caltex Riau Polytechnic pupil Legislative frame (BLM) is a pupil organization that includes out the capabilities of budgeting, regulation and supervision. the selection of prospective PCR BLM contributors is still conventional, such as selecting scholar documents one after the other which results in problems in organizing scholar documents. To help the manner of choosing BLM PCR members in figuring out the selected BLM PCR individuals, a choice-making gadget is wanted that may be used as an opportunity consideration between the selection outcomes acquired manually and the outcomes received from the gadget. in addition to being a device to help the pinnacle of BLM in making selections the usage of the Weighted Product approach. based totally at the effects of blackbox testing, it could be concluded that the BLM member selection system works in step with user needs. as well as the consequences of the usability testing, the test consequences obtained with a total percent of ninety two.35% (Strongly Agree). And the outcomes of checking out the accuracy of manual calculations with the system display that the accuracy stage is a hundred%. From the effects of this take a look at it became concluded that the gadget is acceptable to users in order that.

Pengembangan Sistem Informasi Pengajuan ISBN di Perpustakaan Politeknik Caltex Riau dini hidayatul qudsi; Mutia Sari Zulvi
Jurnal Komputer Terapan Vol. 9 No. 2 (2023): Jurnal Komputer Terapan
Publisher : Politeknik Caltex Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35143/jkt.v9i2.6199

The ISBN (International Standard Book Number) application is one of the facilities provided by the Riau Caltex Polytechnic Library (PCR) to the academic community to assist the academic community to get ISBNs from the National Library. Until now, the ISBN application process for the Caltex Riau Polytechnic Publisher (PCR) has taken quite a long time. The author communicates via email sent to library staff, causing the author and staff have difficulty seeing the progress status of the ISBN application. Therefore, a library system was built to manage the ISBN management process for the Caltex Polytechnic Publisher. The system development uses Rapid Application Development (RAD) methodology. The development of the website using the RAD methodology requires a strong commitment between developers and users, so that the system can be completed within the specified time. The resulting system has been tested using the Black Box testing method and states that the system features are 100% running as expected.

WORKSHOP UI/UX MENGGUNAKAN FIGMA UNTUK SISWA/I SMKN 7 PEKANBARU Zulvi, Mutia Sari; Qudsi, Dini Hidayatul; Najwa, Nina Fadilah
JITER-PM (Jurnal Inovasi Terapan - Pengabdian Masyarakat) Vol. 1 No. 4 (2023): JITER-PM
Publisher : Politeknik Caltex Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35143/jiter-pm.v1i4.6208

Dalam menghadapi dunia kerja siswa harus mempersiapkan diri baik ilmu maupun kesiapan mental dan juga pengetahuan akan dunia kerja. peluang kerja sebagai UI/UX desainer merupakan pekerjaan yang memang menjanjikan dalam bidang profesi TI. Dasar Pemrograman web telah dipelajari sejak di bangku sekolah khusus nya sekolah menengah kejuruan, sehingga siswanya sudah memiliki pengetahuan dasar tentang pemrograman web desain. Pengabdian ini mengambil studi kasus di SMKN 7 Pekanbaru untuk jurusan yang berkaitan dengan dunia TI. Pada pembangunan aplikasi berbasis website di SMKN 7 Pekanbaru belum menerapkan konsep UI/UX. Karena desain yang belum menerapkan konsep UI/UX membuat beberapa kendala bagi siswa dalam melakukan pembangunan aplikasi berbasis web. Sehingga, pengabdian ini bertujuan untuk memberikan pemahaman dan kemampuan kepada siswa mengenai konsep UI/UX menggunakan figma. Pengabdian dilaksanakan di SMKN 7 dengan jumlah peserta sebanyak 34 orang dalam satu laboratorium komputer. Berdasarkan hasil pengabdian, pelatihan UI/UX menggunakan figma telah berhasil terlaksana sesuai dengan jadwal yang telah direncakan. Peserta menyatakan bahwa ilmu yang didapatkan dari pelatihan ini sangat bermanfaat dan berharap kedepannya akan dilaksanakan pelatihan serupa dengan waktu yang lebih lama.

Improving Panic Disorder Classification Using SMOTE and Random Forest Nurmalasari, Dini; Yuliantoro, Heri R; Qudsi, Dini Hidayatul
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8315

Panic disorder is a serious anxiety disorder that can significantly impact an individual's mental health. If left undetected, this disorder can disrupt daily life, social relationships, and overall quality of life. Early detection and intervention are crucial for managing panic disorder and improving the well-being of those affected. Technology plays a pivotal role in facilitating early detection through data-driven approaches that employ algorithms to identify patterns of behavior or symptoms associated with panic disorder. Accurate classification of panic disorder is crucial for effective diagnosis and treatment. However, machine learning models trained on imbalanced datasets, such as those containing panic disorder patients, are prone to overfitting, leading to poor generalization performance. This study investigates the effectiveness of the Synthetic Minority Oversampling Technique (SMOTE) in addressing overfitting in panic disorder dataset classification using the Random Forest algorithm. The results demonstrate that SMOTE significantly improves the classification performance of Random Forest. By mitigating overfitting and improving generalization to unseen data, SMOTE increases accuracy by 15 percentage points. Before using SMOTE, the accuracy was 82%, and after using SMOTE it is 97%. The findings underscore the promise of SMOTE as a tool for boosting the performance of machine learning algorithms in classifying panic disorder from imbalanced data.

Discovering User Sentiment Patterns in Libraries with a Hybrid Machine Learning and Lexicon-Based Approach Nurmalasari, Dini; Qudsi, Dini Hidayatul; Chairani, Nessa; Yuliantoro, Heri R
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 3 (2024): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i3.2217

The need to enhance library services is the focus of this study, which relies on user feedback for data-driven decision-making. Text data from library user surveys conducted at Politeknik Caltex Riau (PCR) is analyzed to categorize sentiment and identify areas for improvement. The biannual student and lecturer feedback collected from 2018 to 2023 through the institution's official survey system (survey.pcr.ac.id) is utilized, providing a comprehensive and robust picture of user needs across five years. Sentiment analysis is employed using the VADER method to classify user comments into positive or negative categories. Text preprocessing techniques, such as stemming, tokenizing, and filtering, are performed to ensure robust classification. Machine learning algorithms – Naïve Bayes, Support Vector Machine (SVM), and Random Forest – are then utilized to evaluate sentiment classification accuracy. The study offers significant findings. Both SVM and Random Forest achieve an outstanding accuracy of 99%, indicating highly reliable sentiment categorization. Notably, these algorithms also achieve 100% precision, recall, and F1-score, demonstrating their effectiveness in accurately identifying positive and negative user sentiment. While Naïve Bayes shows slightly lower accuracy at 98%, it maintains a high recall rate (100%), ensuring all negative feedback is captured. This research presents a novel approach combining user sentiment analysis with a comprehensive five-year dataset. This enables a deeper understanding of evolving user needs and priorities. The high accuracy and effectiveness of the employed algorithms highlight the potential of this methodology for libraries. Libraries can leverage user feedback for evidence-based service improvement and increased user satisfaction.

Discovering User Sentiment Patterns in Libraries with a Hybrid Machine Learning and Lexicon-Based Approach Nurmalasari, Dini; Qudsi, Dini Hidayatul; Chairani, Nessa; Yuliantoro, Heri R
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 3 (2024): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i3.2217

The need to enhance library services is the focus of this study, which relies on user feedback for data-driven decision-making. Text data from library user surveys conducted at Politeknik Caltex Riau (PCR) is analyzed to categorize sentiment and identify areas for improvement. The biannual student and lecturer feedback collected from 2018 to 2023 through the institution's official survey system (survey.pcr.ac.id) is utilized, providing a comprehensive and robust picture of user needs across five years. Sentiment analysis is employed using the VADER method to classify user comments into positive or negative categories. Text preprocessing techniques, such as stemming, tokenizing, and filtering, are performed to ensure robust classification. Machine learning algorithms – Naïve Bayes, Support Vector Machine (SVM), and Random Forest – are then utilized to evaluate sentiment classification accuracy. The study offers significant findings. Both SVM and Random Forest achieve an outstanding accuracy of 99%, indicating highly reliable sentiment categorization. Notably, these algorithms also achieve 100% precision, recall, and F1-score, demonstrating their effectiveness in accurately identifying positive and negative user sentiment. While Naïve Bayes shows slightly lower accuracy at 98%, it maintains a high recall rate (100%), ensuring all negative feedback is captured. This research presents a novel approach combining user sentiment analysis with a comprehensive five-year dataset. This enables a deeper understanding of evolving user needs and priorities. The high accuracy and effectiveness of the employed algorithms highlight the potential of this methodology for libraries. Libraries can leverage user feedback for evidence-based service improvement and increased user satisfaction.

Transformasi Digital Manajemen Pelatihan Internal Staf dengan Pendekatan Extreme Programming Tasya Nurul Fadilah; Santoso, Heni Rachmawati; Qudsi, Dini Hidayatul
Jurnal Komputer Terapan Vol 11 No 1 (2025): Jurnal Komputer Terapan
Publisher : Politeknik Caltex Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35143/jkt.v11i1.6662

Internal training activities at the Information Technology Department of Politeknik Caltex Riau were previously managed in a semi-manual manner without an integrated database, leading to inefficiencies in administrative processes such as identifying potential trainers, participant registration, material documentation, and reporting. These issues resulted in disorganized data management and the risk of losing important information. This study aims to develop a web-based Training Management Information System using the Extreme Programming (XP) method as a solution to improve efficiency and service quality. The development process follows the XP stages: exploration (identifying user needs), planning (scheduling and estimating resources), development iterations (analysis, design, incremental releases, and feedback), and final production (system release and refinement). System evaluation was conducted through usability testing using questionnaires for organizers and lecturers, as well as in-depth interviews with the Head of Department as a strategic user. The results show that 79% of respondents found the system easy to use, relevant to their needs, and functionally adequate. Interviews also confirmed that the system met expectations in terms of features and interface design. These findings indicate that the XP approach is effective in developing user-centered web-based systems and improving the operational efficiency of internal training activities.

Information Gain Feature Selection for Temporal Sentiment Analysis of Pedulilindungi Application Review using Naïve Bayes Classifier Algorithm: Information Gain Feature Selection for Temporal Sentiment Analysis of Pedulilindungi Application Review using Naïve Bayes Classifier Algorithm Helma, Siti Syahidatul; Qudsi, Dini Hidayatul; Chatisa, Ivan
Indonesian Journal of Informatic Research and Software Engineering (IJIRSE) Vol. 5 No. 2 (2025): Indonesian Journal of Informatic Research and Software Engineering (IJIRSE)
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/ijirse.v5i2.2217

Through the Instruction of the Minister of Home Affairs of the Republic of Indonesia Number 38 of 2021 concerning the Implementation of Restrictions on Community Activities (PPKM), all communities are required to use the Pedulilindungi application from August 31, 2021, to September 6, 2021, and updated regularly. Users can download and access the Pedulilindungi application through the Google Play Store application market. There, users can directly assess an application by providing reviews that can describe user responses and satisfaction with the application. The Naïve Bayes Classifier (NBC) algorithm is applied to perform modeling in classifying temporal sentiment analysis data. Prior to classification, a feature selection process with information gain is performed. Based on the experimental results, the best evaluation was produced on temporal data dated September 03, 2021, with an accuracy of 91.9% and precision and recall values of 99.9% and 91.9%, respectively.

Analisis Sentimen pada Data Saran Mahasiswa Terhadap Kinerja Departemen di Perguruan Tinggi Menggunakan Convolutional Neural Network Yuliska, Yuliska; Qudsi, Dini Hidayatul; Lubis, Juanda Hakim; Syaliman, Khairul Umum; Najwa, Nina Fadilah
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8 No 5: Oktober 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2021854842

Review atau saran dari customer dapat menjadi sangat penting bagi penyedia layanan, begitu pula saran dari mahasiswa mengenai layanan sebuah unit kerja di perguruan tinggi. Review menjadi penting karena dapat menjadi indikator kinerja penyedia layanan. Pengolahan review juga sangat penting karena dapat menjadi referensi untuk pengambilan keputusan dan peningkatan layanan yang lebih baik ke depannya. Penelitian ini menerapkan analisis sentimen pada data saran atau review mahasiswa terhadap kinerja unit kerja atau departemen di perguruan tinggi, yaitu Politeknik Caltex Riau. Analisis sentimen dilakukan dengan menggunakan Convolutional Neural Network (CNN) dan word embedding Word2vec sebagai representasi kata. CNN merupakan metode yang memiliki performa yang baik dalam mengklasifikasi teks, yaitu dengan teknik convolutional yang menggabungkan beberapa window kata pada kalimat dan mengambil window yang paling representative. Word2Vec digunakan sebagai representasi data saran dan inputan awal pada CNN, dimana Word2Vec merupakan dense vectors yang dapat merepresentasikan hubungan antar kata pada data saran dengan baik. Saran mahasiswa dapat mengandung kalimat yang sangat panjang, karena itu perpaduan Word2Vec sebagai representasi kata dan CNN dengan teknik convolutional, dapat menghasilkan representasi yang representative dari kalimat panjang tersebut. Penelitian ini menggunakan dua arsitektur CNN, yaitu Simple CNN dan DoubleMax CNN untuk mengidentifikasi pengaruh kompleksitas arsitektur terhadap hasil klasifikasi sentimen. Berdasarkan hasil pengujian, DoubleMax CNN dapat mengklasifikasi sentimen pada saran mahasiswa dengan sangat baik, yaitu mencapai Akurasi tertinggi sebesar 98%, Recall 97%, Precision 98% dan F1-Score 98%. AbstractStudent’s reviews about department performance can be essential for a college for it can be used to evaluate the department performance and to take an immediate action to improve its performance. This research applies sentiment analysis in the student’s reviews of college department in Politeknik Caltex Riau. Convolutional Neural Network and Word2Vec are employed to analyze the sentiment. CNN is known for its good performance in text classification by applying a convolutional technique to the input sentences. Word2Vec is used as word representation and as an input to the CNN. Word2Vec are dense vectors which can represent the relationship between words excellently. Student’s reviews can be a long sentence; hence the combination of Word2Vec as word representation and CNN with convolutional technique can produce a representative fiture from that long sentence. This research utilizes two CNN architectures, which are Simple CNN dan DoubleMax CNN to identify the effect of the complexity of CNN architecture to final result. Our experiments show that DoubleMax CNN has a great performance in classifying sentiment in the student’s reviews with the best Accuracy value of 98%, Recall 97%, Precision 98% and F1-Score value of 98%.

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