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Drafting of Online Meeting Minutes Based on Video Recording Using Topic Modelling Rakhmat Arianto; Alwy Abdullah; Usman Nurhasan; Rokhimatul Wakhidah
SISFORMA Vol 10, No 1: May 2023
Publisher : Soegijapranata Catholic University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24167/sisforma.v10i1.5037

Meeting minutes are important because they can track decisions and agreements made during the meeting. Meeting minutes can also be used as a benchmark for whether the meeting objectives have been achieved or not. Minutes are taken during the meeting until the end of the meeting, which contains essential points from the meeting. Minutes in online meetings are currently still done manually, and generally, every meeting is recorded as documentation that requires more Human Resources to change the recording of the meeting file Based on the problems above, a solution to this problem is needed by creating an automatic note-taking system that can assist the note-takers in concluding the meeting, especially in the Information Technology Department. This study uses the Latent Dirichlet Allocation (LDA) method to determine topic modeling. Based on this research, the system calculation using the LDA method produces the results obtained on the coherence score and similarity score only get an average value of 64.56% and 57.91% where these values are still less than optimal if used in actual conditions.

Automatic notes based on video records of online meetings using the latent Dirichlet allocation method Arianto, Rakhmat; Asmara, Rosa Andrie; Nurhasan, Usman; Rahmanto, Anugrah Nur
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4147-4153

Meeting minutes can also be used as a benchmark for whether the meeting objectives have been achieved or not. Minutes are taken during the meeting until the end of the meeting, which contain essential points from the meeting. Minutes in online meetings are currently still done manually, and generally, every meeting is recorded as documentation that requires more human resources to change the recording of the meeting file. Based on the problems above, a solution to this problem is needed by creating an automatic note-taking system that can assist the note-takers in concluding the meeting, especially in the Information Technology Department. This study uses the latent Dirichlet allocation (LDA) method to determine text summarization and topic modeling. Based on this research, the system calculation using the LDA method produces a pretty good accuracy value for text summarization of 57.91% and topic modeling with a coherence score of 64.56%. Based on this research, the implementation of the latent Dirichlet allocation method for text summarization and topic modeling provides a fairly good level of similarity accuracy when compared to the minutes that are written manually and can be implemented in the Information Technology Department.

Analisis Sentimen Ulasan Pembeli pada Toko Skincare Lokal Marketplace Tokopedia menggunakan Metode Sentistrength dan Naïve Bayes Classifier Hasyimiyyah, Nisrina; Mashudi, Irsyad Arif; Arianto, Rakhmat
KOMPUTEK Vol 8, No 2 (2024): Oktober
Publisher : Universitas Muhammadiyah Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24269/jkt.v8i2.2893

Marketplace is an online buying and selling place increasingly in demand by Indonesians. One of the marketplaces with the most visits according to SimilarWeb data is Tokopedia. On Tokopedia, there is a review feature where buyers can send their opinions as criticism or suggestions. Local skincare brands in this research include Avoskin, Azarine, Skin Game, and Somethinc. Buyer reviews listed in each store are triggers for transactions. The purpose of this research is to analyze the sentiment of buyer reviews at local skincare stores on Tokopedia using the SentiStrength method and Naïve Bayes Classifier. Sentiment analysis is carried out to divide buyer review data into negative, neutral, and positive sentiments using a model created with Naïve Bayes Classifier with training and testing data labeled manually using the SentiStrength ID dictionary. Data collection was done using web scraping of 247 data from four stores. The sentiment prediction model uses dataset labeling with SentiStrength ID and a Naïve Bayes Classifier. The process involves the use of complete stopwords without stemming. This model achieved a training accuracy of 94%. However, the testing accuracy only reached 68%. Based on data scraping from 300 reviews, Avoskin has 151 positive reviews, 67 negative reviews, and 8 neutral reviews. Meanwhile, Azarine has 152 positive reviews, 58 negative reviews, and 8 neutral reviews. Skin Game has 186 positive reviews, 54 negative reviews, and 17 neutral reviews. Somethinc has 187 positive reviews, 47 negative reviews, and 20 neutral reviews.

Penerapan Vector Space Model Dalam Klasifikasi Penilaian Thematic Appeception Test Nurhasan, Usman; Arianto, Rakhmat; Kurnia, Alwan Ghozi
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 5, No 2 (2021): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v5i2.356

Psychology is the study of human mental behavior and functions scientifically. In practice, a person's personality can be assessed from psychological tests. One of the psychological tests is the Thematic Apperception Test (TAT). TAT Test is a projective psychological test consisting of various themes presented in the form of an image which is then projected accordingly with the response. The purpose of the TAT is to reveal the dynamics of the subject's personality in the form of encouragement, sediment, complex, and various dominant conflicts. Thematic Apperception Test still uses a card and tape recorder to record Testee's voice. Calculation of results or assessments is still done manually. Errors in the assessment will affect the results, so we need an intelligent information system using the vector space model method based on experience in the field of psychology. A web-based system which can provide personality test results and information about the Thematic Apperception Test. From the test results using 10 stories, it was found that the average precission value was 72.7%, the average recall value was 86.7%, and the average f-measure value was 78.3%.

Penerapan Vector Space Model Dalam Klasifikasi Penilaian Thematic Appeception Test Nurhasan, Usman; Arianto, Rakhmat; Kurnia, Alwan Ghozi
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 5, No 2 (2021): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v5i2.356

Psychology is the study of human mental behavior and functions scientifically. In practice, a person's personality can be assessed from psychological tests. One of the psychological tests is the Thematic Apperception Test (TAT). TAT Test is a projective psychological test consisting of various themes presented in the form of an image which is then projected accordingly with the response. The purpose of the TAT is to reveal the dynamics of the subject's personality in the form of encouragement, sediment, complex, and various dominant conflicts. Thematic Apperception Test still uses a card and tape recorder to record Testee's voice. Calculation of results or assessments is still done manually. Errors in the assessment will affect the results, so we need an intelligent information system using the vector space model method based on experience in the field of psychology. A web-based system which can provide personality test results and information about the Thematic Apperception Test. From the test results using 10 stories, it was found that the average precission value was 72.7%, the average recall value was 86.7%, and the average f-measure value was 78.3%.

Integrasi Sistem Informasi Geografis dan Sistem Informasi Manajemen Keanggotaan untuk Meningkatkan Aksesibilitas Layanan Kesehatan pada Ikatan Dokter Indonesia (IDI) Cabang Malang Raya Rozi, Imam Fahrur; Ariyanto, Rudy; Arianto, Rakhmat; Hapsari, Ratih Indri; Ananta, Ahmadi Yuli; Rohadi, Erfan; Widito, Sasmojo; Zakaria, Arief Syukron; Budiarti, Arry; Saputra, Zainal Ulu Prima; Irawan, Ferry Buyung Bakhtiar; Sholiha, Afifah
J-Dinamika : Jurnal Pengabdian Masyarakat Vol 10 No 1 (2025): April
Publisher : Politeknik Negeri Jember

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

The Indonesian Medical Association (IDI) Chapter Malang Raya, which covers area of Malang City, Batu City, and Malang Regency, faces challenges in managing doctor membership data and presenting information related to customer services and public services. To overcome these obstacles, a website-based information system was developed that integrates the Membership Management Information System with the Geographic Information System (GIS). The Membership Management Information System facilitates efficient management data of physician member of IDI chapter Malang Raya, including status of membership, competence of medical doctors, speciality, and subspeciality. Whereas GIS system serves to map the location of doctor practices. Integration these two systems making it easier for people to find the nearest health services. The system was developed using the waterfall methodology, which involves the stages of requirements analysis, design, implementation, testing and maintenance. The result is a platform that can improve IDI's internal efficiency and make it easier for people to access health services. This system has the potential to be further developed with the addition of security features and functionality such as real-time monitoring and mobile application integration, thus supporting more responsive and integrated health services

Analisis Sentimen Ulasan Pembeli pada Toko Skincare Lokal Marketplace Tokopedia menggunakan Metode Sentistrength dan NaÃ¯ve Bayes Classifier Hasyimiyyah, Nisrina; Mashudi, Irsyad Arif; Arianto, Rakhmat
KOMPUTEK Vol. 8 No. 2 (2024): Oktober
Publisher : Universitas Muhammadiyah Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24269/jkt.v8i2.2893

Marketplace is an online buying and selling place increasingly in demand by Indonesians. One of the marketplaces with the most visits according to SimilarWeb data is Tokopedia. On Tokopedia, there is a review feature where buyers can send their opinions as criticism or suggestions. Local skincare brands in this research include Avoskin, Azarine, Skin Game, and Somethinc. Buyer reviews listed in each store are triggers for transactions. The purpose of this research is to analyze the sentiment of buyer reviews at local skincare stores on Tokopedia using the SentiStrength method and NaÃ¯ve Bayes Classifier. Sentiment analysis is carried out to divide buyer review data into negative, neutral, and positive sentiments using a model created with NaÃ¯ve Bayes Classifier with training and testing data labeled manually using the SentiStrength ID dictionary. Data collection was done using web scraping of 247 data from four stores. The sentiment prediction model uses dataset labeling with SentiStrength ID and a NaÃ¯ve Bayes Classifier. The process involves the use of complete stopwords without stemming. This model achieved a training accuracy of 94%. However, the testing accuracy only reached 68%. Based on data scraping from 300 reviews, Avoskin has 151 positive reviews, 67 negative reviews, and 8 neutral reviews. Meanwhile, Azarine has 152 positive reviews, 58 negative reviews, and 8 neutral reviews. Skin Game has 186 positive reviews, 54 negative reviews, and 17 neutral reviews. Somethinc has 187 positive reviews, 47 negative reviews, and 20 neutral reviews.

Comparison of Feature Extraction in Support Vector Machine (SVM) Based Sentiment Analysis System Rozi, Imam Fahrur; Maulidia, Irma; Hani’ah, Mamluatul; Arianto, Rakhmat; Yunianto, Dika Rizky; Ananta, Ahmadi Yuli
Jurnal Ilmiah Kursor Vol. 13 No. 1 (2025)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v13i1.417

Sentiment analysis plays a crucial role in natural language processing by identifying and categorizing opinions or emotions conveyed in textual data. It is widely applied across diverse fields such as product review analysis, social media monitoring, and market research. To enhance the accuracy and reliability of sentiment classification, various methods and feature extraction techniques have been explored. This study investigates the use of Support Vector Machine (SVM) for sentiment analysis, comparing three feature extraction techniques: Term Frequency-Inverse Document Frequency (TF-IDF), Bag of Words (BoW), and Word2Vec. Our findings indicate that SVM performs effectively with all three feature extraction methods, with TF-IDF yielding the highest accuracy at 0.79. Although the BoW method showed competitive results, it slightly trailed TF-IDF in k-fold validation. Word2Vec, however, exhibited the lowest performance, achieving a maximum accuracy of 0.69. A comparative analysis of accuracy, precision, recall, and F1-score highlight the superiority of TF-IDF in delivering consistent and accurate results. Further statistical analysis using ANOVA revealed no significant differences between the models across any of the evaluation metrics. Additionally, the evaluation was conducted under several scenarios, including tests on balanced and imbalanced datasets, varying dataset sizes, and different CCC parameter values for SVM. These scenarios provided deeper insights into the factors influencing the system's performance, reinforcing that TF-IDF combined with SVM remains the most effective approach in this study.

Analisis Performa Metode Extreme Learning Machine dan Multiple Linear Regression dalam Prediksi Produksi Gula Ananta, Ahmadi Yuli; Ariyanto, Rudy; Rozi, Imam Fahrur; Arianto, Rakhmat
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 1 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i1.29626

Sugar is a crucial commodity in Indonesia, with demand increasing annually. Variations in sugar production require accurate prediction strategies for industrial planning. This study aims to analyze the performance of the Extreme Learning Machine (ELM) and Multiple Linear Regression (MLR) methods in predicting sugar production. This research employs a quantitative experimental approach, with sugar production data during the 2020-2023 milling period as the research subject. Data collection techniques involve observation and documentation, while data analysis techniques utilize Mean Absolute Percentage Error (MAPE) and 10-Fold Cross-Validation to measure model accuracy. The results indicate that ELM has a lower error rate (MAPE 16.06%) compared to MLR (MAPE 27.90%), making it more effective in capturing complex sugar production patterns. Implementing this model in a web-based system also enables more efficient production monitoring. The ELM method proves to be superior in predicting sugar production and can be integrated into industrial systems to support data-driven decision-making. Future research can explore other predictive models, such as deep learning, and consider external factors like weather and soil conditions to enhance accuracy.

THE IMPLEMENTATION OF THE OJIR APPLICATION AT CENTRAL WASTE BANK IN MALANG DISTRICT FOR THE MANAGEMENT OF HOUSEHOLD-SCALE ORGANIC WASTE DIVERSIFICATION PROCESSES Nurhasan, Usman; Rohadi, Erfan; Nurindrasari, Diana; Arianto, Rakhmat; Pradibta, Hendra
Abdi Dosen : Jurnal Pengabdian Pada Masyarakat Vol. 7 No. 4 (2023): DESEMBER
Publisher : LPPM Univ. Ibn Khaldun Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32832/abdidos.v7i4.2163

The Ojir application, a mobile-based innovation for organic waste management at Central Malang Waste Bank (BCM), provides an effective solution to streamline the collection and management of organic waste. Addressing environmental concerns such as food scraps, dry leaves, and fruits, the application responds to Ministry of Environment and Forestry (KLHK) data indicating that approximately 61.50% of total waste is organic, necessitating efficient management to reduce waste volume. Established in 2016, BCM faced challenges in manual data management and limited organic waste search capabilities. To address these issues, Ojir was introduced to modernize the organic waste management system. This innovation surpasses traditional waste management by incorporating a process of diversifying organic waste into maggot raw material. Beyond resolving existing waste bank issues, Ojir enhances management efficiency, allowing users to easily track and manage organic waste with increased accuracy. Consequently, the paradigm of organic waste management shifts towards modernity, effectiveness, and efficiency. Aligned with efforts to create a clean and sustainable environment, Ojir represents an innovative step in reinforcing the role of waste banks in handling organic waste in the technological era. The incorporation of the maggot raw material process further exemplifies the forward-thinking approach in sustainable waste management

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