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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Sistem Komputer TELKOMNIKA (Telecommunication Computing Electronics and Control) International Journal of Advances in Intelligent Informatics Register: Jurnal Ilmiah Teknologi Sistem Informasi Indonesia Symposium on Computing IJoICT (International Journal on Information and Communication Technology) JOIV : International Journal on Informatics Visualization Jurnal Penelitian Pendidikan IPA (JPPIPA) Seminar Nasional Teknologi Informasi Komunikasi dan Industri JSiI (Jurnal Sistem Informasi) Jurnal Teknologi dan Informasi Jurnal Mantik Ihsan: Jurnal Pengabdian Masyarakat Journal of Applied Data Sciences Journal of Dinda : Data Science, Information Technology, and Data Analytics eProceedings of Applied Science eProceedings of Engineering Jurnal INFOTEL
Alfian Akbar Gozali
Telkom University
Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Physics Social Sciences

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Implementasi Fulltext Indexing pada Dokumen Elektronik dengan Algoritma B-Tree Diken Pradana Putra; Eko Darwiyanto; Alfian Akbar Gozali
eProceedings of Engineering Vol 2, No 1 (2015): April, 2015
Publisher : eProceedings of Engineering

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Abstract

Dokumen merupakan sumber informasi yang mengandung fakta penting dari suatu kejadian atau keadaan tertentu dan dokumen tersebut menjadi suatu informasi penting bagi suatu instansi. Penggunaan dokumen elektronik sudah menggeser penggunaan dokumen konvensional yang memakai kertas sebagai bentuk fisiknya. Pengelolaan dokumen elektronik dapat dilakukan dengan menyimpannya pada media penyimpanan offline (media magnetik dan media optik) maupun online (database online dan cloud storage) yang mana keduanya memiliki fungsi indexing sebagai metode pengelolaannya. Salah satu metode indexing untuk meng-index teks biasa agar mengurangi kapasitas pemakaian storage dan meningkatkan kinerja searching adalah Fulltext Indexing. Dalam Fulltext Indexing indeks disimpan dalam struktur Balance Search Tree (B-Tree), dimana struktur penyimpanan database ini memudahkan Indexing dan Searching dokumen.Hasil penelitian Tugas Akhir ini adalah pengimplementasian Fulltext Indexing dan struktur B-Tree membuat sistem pengelolaan dokumen elektronik menjadi lebih cepat 0,3 kali dibandingkan tanpa pengimplementasian kedua metode tersebut dengan perbandingan jumlah kata ter-index dengan jumlah kata dari jumlah dokumen yang ditentukan adalah 1:8,6. Kata Kunci : Dokumen Elektronik, Fulltext Indexing, B-Tree
Analisa Dan Implementasi Graph Summarization Dengan Metode Canal Wisnu Riyan Pratama Putra; Kemas Rahmat Saleh Wiharja; Alfian Akbar Gozali
eProceedings of Engineering Vol 2, No 2 (2015): Agustus, 2015
Publisher : eProceedings of Engineering

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Abstract— Pemodelan data menggunakan graph telah diterapkan oleh banyak aplikasi dan sistem berskala besar dalam berbagai bidang. Data tersebut direpresentasikan sebagai graph dengan node yang mewakili sebuah objek dan edge menandakan hubungan antara dua objek. Untuk memahami karakteristik graph, maka dibutuhkan teknik graph summarization. Pada penelitian ini digunakan metode CANAL (Categorization of Attributes with Numerical Values based on Attribute Values and Link Structures of Nodes) untuk meringkas graph. Metode ini merupakan pengembangan dari metode Aggregation-Based Graph Summarization yang melakukan peringkasan dengan mengelompokkan serta menggabung node kedalam sebuah super node kemudian mengggali pengetahuan dari data untuk menemukan cutoff yang digunakan dalam pengelompokan node secara otomatis. Metode CANAL memperbaiki metode graph summarization SNAP dan k-SNAP yang masih mempunyai kelemahan dalam menangani data dengan atribut numerik[2]. Kedua metode tersebut hanya dapat menangani categorical node attribute, sehingga ketika dihadapkan dengan atribut numerik pengguna masih harus melakukan pengelompokan secara manual berdasarkan pengetahuan mereka terhadap data yang digunakan. Hasil dari sistem yang akan dibangun merupakan sebuah graph summary yang merepresentasikan pattern hubungan antar kelompok dalam ringkasan. Pattern tersebut dapat digunakan untuk membantu memahami informasi yang tersembunyi didalam graph asli. Dari summary yang dihasilkan oleh metode CANAL kemudian dinilai kualitasnya dan dibandingkan dengan kualitas summary dengan cutoff manual. Perbandingan tersebut menunjukkan bahwa kualitas summary dari CANAL memiliki kualitas baik yang setara dengan kualitas summary dengan cutoff manual. Keywords—graph summarization, Aggregation-Based Graph Summarization, node attribute, link structure, interestingness measure.
Implementasi Prinsip MDL untuk Kompresi Graph Database Menggunakan Algoritma Greedy Harris Febryantony Z; Kemas Rahmat Saleh Wiharja; Alfian Akbar Gozali
eProceedings of Engineering Vol 2, No 1 (2015): April, 2015
Publisher : eProceedings of Engineering

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Abstract

Graph secara konsep merupakan abstraksi yang secara fundamental telah lama dipakai, yang memungkinkan untuk memodelkan pada sistem di dunia nyata. Begitu pula pada data, data jenis apapun dapat dimodelkan relasi antar data tersebut menggunakan graph. Graph database diadopsi untuk memudahkan dan membantu dalam memahami, memodelkan, serta menganalisis suatu proses. Graph database sangat cocok digunakan pada data bersifat tidak terstruktur dan semi terstruktur dibanding relational database yang mana memiliki kelemahan jika data dan ukuran tabel bertambah menyebabkan kemungkinan join antar tabel sangat besar. Dalam aplikasinya jumlah data pada graph database semakin lama akan berkembang semakin besar menjadi jutaan bahkan miliaran node dan edge, sehingga cost untuk untuk melakukan analisa dan visualisasi graph databse menjadi sangat besar untuk kemampuan sistem saat ini. Untuk menyelesaikan permasalahan tersebut maka diperlukan suatu metode untuk mengurangi ukuran dari graph tetapi tetap menyimpan informasi-informasi penting dari graph. Dengan menerapkan prinsip Rissaenen’s Minimum Description Length (MDL) dan melakukan penggabungan secara greedy serta mengombinasikan dengan representasi graph G yang terdiri dari Graph Summary dan sebuah set Correction, maka dapat dihasilkan graph database yang dikompres dengan baik. Kata Kunci: graph database, graph summarization, graph representation, MDL principle, lossles, lossy, compression, greedy, Rissaenen’s Minimum Description Length
Color blind assistant app based on computer vision using openCV Fahri Alfiansyah; Alfian Akbar Gozali; Akmal Natakusuma
Jurnal Mantik Vol. 7 No. 2 (2023): Agustus: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v7i2.3889

Abstract

One of the health problems is color blindness. This disease is the inability to determine certain types of colors. The number of people with color blindness in Indonesia is increasing every year. Based on this problem, a color detection application was created to help color blind people based on computer vision. This application is made to be able to detect the color of objects in real-time using OpenCV library to detect color, taking objects using a smartphone camera. This application can detect an object moreover it can display color information in the form of sound from the smartphone. The color detection application aims to enhance the daily lives of color blind individuals especially in Indonesia by assisting them in perceiving and distinguishing colors in real-time, thereby promoting inclusivity and improving their overall experiences.
Multi-Temporal Factors to Analyze Indonesian Government Policies regarding Restrictions on Community Activities during COVID-19 Pandemic Fachri Pane, Syafrial; Adiwijaya, Adiwijaya; Dwi Sulistiyo, Mahmud; Akbar Gozali, Alfian
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.7.4.2415

Abstract

Concerning the implementation of the government policy regarding the Restriction of Community Activities (PPKM) during the COVID-19 pandemic era, there are still discrepancies in the economic sector and population mobility. This issue emerges due to irrelevant data and information in one region of Indonesia. The data differences should be carefully solved when implementing the PPKM policy. Besides, the PPKM must also pay attention to some specific factors related to the real conditions of a region, such as the data on the epidemiology of COVID-19, economic situations, and population mobility. These three are called Multi Factors. Then, based on the data, COVID-19 has a specific spreading period that cannot be repeated and thus is called temporal. Therefore, using the Multi-Temporal Factors approach to identify their correlation with the PPKM policy by applying Machine Learning, such as the Multiple Linear Regression model and Dynamic Factors, is essential. This research aims to analyze the characteristics and correlations of the COVID-19 pandemic data and the effectiveness of the government's policy on community activities (PPKM) based on the data quality. The results show that the accuracy of the multiple linear regression models is 84%. The Dynamic Factor shows that the five most important factors are idr_close, positive, retail_recreation, station, and healing. Based on the ANOVA test, all independent variables significantly influence the dependent one. The linear multiple regression models do not display any symptoms of heteroscedasticity. Thus, based on the data quality, the implementation of PPKM by the government has a practical impact.
Hangout Places Recommendation System Using Content-Based Filtering and Cosine Similarity Methods Abdul Raihan; Ahmad Ibrahim A.M; Alfian Akbar Gozali
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 4 No 2 (2024): August
Publisher : Research Group of Data Engineering, Faculty of Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/dinda.v4i2.1464

Abstract

Coffee shops are becoming the new normal for friends and coworkers to hang out. Selecting the ideal location to hang out can be exceedingly difficult. There are too many choices, and it can be difficult to know where to begin. Based on this problem, a web application that responds to the growing need for an easy method of finding local hangouts is named Nongkies. With a focus on social interaction and exploration, this platform uses a recommender system to find cafes, restaurants, and entertainment venues easily. Key features include location-based search, category, and details places. Extensive testing has confirmed the reliability of Nongkies, offering user-friendly and accurate search results. This system is a website app that suggests places to users based on their preferences. This application was developed using the cosine similarity method, which is a systematic approach that uses a similar method based on cosine angles. Content that is less alike gets lower rankings, while more similar content gets the highest rankings in recommendations. Moreover, this app helps users find local hangouts and directions to those locations, especially university students, and the selection of places to socialize has a significant effect on students' learning experiences.
Mobile Assistant Application for Street Food Consumers in Bandung Julius Angger Satrio Wicaksono; Kadek David Kurniawan; Alfian Akbar Gozali
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 4 No 2 (2024): August
Publisher : Research Group of Data Engineering, Faculty of Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/dinda.v4i2.1470

Abstract

In the dynamic city of Bandung, the lively street food scene has captured the fascination of tourists, offering a diverse selection of tempting dishes. Nevertheless, a persistent challenge arises from the lack of comprehensive details about these street foods, presenting a hurdle for consumers in making well-informed and health-conscious choices. This predicament underscores the necessity for a solution, leading to the introduction of the Mobile Assistant Application for Street Food Consumers in Bandung. Harnessing cutting-edge computer vision technology, this application seeks to provide a solution by furnishing users with an intuitive and effective tool for accessing in-depth information regarding street foods. The outcomes of thorough experimentation highlight the application's success in precisely identifying a wide array of street foods in Bandung. Users benefit from accurate information on ingredients and nutritional values, empowering them to make informed dietary decisions and elevating the overall street food experience in Bandung. This inventive solution not only addresses the prevailing information gap but also contributes to the well-being of consumers, ushering in a healthier and more enlightened food culture in Bandung at the tip of one's finger.
Development of Palm Oil Production and Sales Monitoring System Based On Android Chikal Fachdiana; Rafie Novianto Sudrajat; Alfian Akbar Gozali
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 4 No 2 (2024): August
Publisher : Research Group of Data Engineering, Faculty of Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/dinda.v4i2.1473

Abstract

Palm oil is one of the most widely used vegetable oils in the world. It is used as a raw material for the economic area and contributes to foreign exchange earnings. The palm oil enterprise performs a critical position in Indonesia's economic development, lowering poverty and creating different businesses supporting the enterprise. This paper aims to assist in improving forecasting, essential factor identification, early caution structures, overall performance monitoring, and decision help for bunches of palm production. in this paper, a machine based totally on system learning is created and applied in order to estimate palm production using models with algorithm decision tree and timeseries.
Enhancing SMOTE Using Euclidean Weighting for Imbalanced Classification Dataset Ramadhan, Nur Ghaniaviyanto; Maharani, Warih; Gozali, Alfian Akbar; Adiwijaya, Adiwijaya
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.798

Abstract

Class imbalance is a significant challenge in machine learning classification tasks because it often causes models to be biased toward the majority class, resulting in poor detection of minority classes. This study proposes a novel enhancement to the Synthetic Minority Over-sampling Technique (SMOTE) by incorporating Euclidean distance-based feature weighting, called Weighted SMOTE. The key idea is to improve the quality of synthetic minority samples by calculating feature importance using a Random Forest model and assigning higher weights to the most relevant features. The objective of this research is to generate more representative synthetic data, reduce model bias, and increase predictive accuracy on highly imbalanced datasets. Experiments were conducted on four benchmark datasets from the KEEL Repository with imbalance ratios ranging from 0.013 to 0.081. The proposed Weighted SMOTE combined with an ensemble voting classifier (Random Forest, AdaBoost, and XGBoost) demonstrated significant improvements compared to standard SMOTE and models without resampling. For example, on the Zoo-3 dataset, the Balanced Accuracy Score (BAS) increased from 75% to 90%, while the F1-score improved from 48% to 94%. On the Cleveland-0_vs_4 dataset, precision improved from 83% to 91% and recall remained high at 99%. Statistical testing using the Wilcoxon signed-rank test confirmed these improvements with p-values 0.05 for key metrics. The findings show that the proposed method effectively balances sensitivity and precision, generates more meaningful synthetic samples, and reduces the risk of overfitting compared to conventional oversampling. The novelty of this work lies in integrating Euclidean-based feature weighting into the SMOTE process and validating its performance on multiple domains with varying feature types and imbalance ratios. These results indicate that the proposed Weighted SMOTE approach contributes a practical solution for improving classification performance and model stability on severely imbalanced data.
The Utilizing GPT-4o Mini in Designing a WhatsApp Chatbot to Support the New Student Admission Process at Telkom University Ruhallah, Muhammad Lutfi; Pratami, Rahmat; Gozali, Alfian Akbar
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 5 No 2 (2025): August
Publisher : Research Group of Data Engineering, Faculty of Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/dinda.v5i2.1963

Abstract

The rapid adoption of Artificial Intelligence (AI) in higher education has revolutionized student support services, yet delivering scalable, real-time assistance through familiar platforms remains a challenge. This study presents the design, implementation, and evaluation of a WhatsApp-based chatbot powered by a fine-tuned GPT-4o Mini model to streamline the new student admission process at Telkom University. A specialized dataset comprising frequently asked questions and admission-related dialogues was curated and preprocessed for model fine-tuning over 288 epochs. The chatbot system integrates the WhatsApp Business API, a Webhook interface, and the n8n automation platform, all deployed on a Virtual Private Server (VPS) to ensure reliability and low-latency communication. Functional and performance testing involved manual scenario-based assessments and quantitative measurements of response accuracy and latency. Results indicate that the chatbot consistently delivers contextually relevant answers—achieving an average accuracy above 85%—and reduces average response time to under 3 seconds. User interaction studies with prospective and current students revealed high satisfaction levels, highlighting improvements in accessibility and reduction of administrative workload. Challenges identified include occasional misinterpretation of complex queries and the need for enhanced scalability under peak loads. Future work will focus on periodic dataset updates, advanced prompt engineering, scalability stress testing, and the integration of multimodal features such as voice and image recognition. By aligning AI-driven conversational interfaces with users’ existing digital habits, this chatbot demonstrates a viable approach for enhancing admission services and operational efficiency in Indonesian higher education institutions.
Co-Authors A, Subaveerapandiyan Abduh Husaini Batubara, Muhammad Abdul Raihan Achmad Rizal Ade Romadhony Adiwijaya Ahmad Ibrahim A.M Akmal Natakusuma Amir Hasanudin Fauzi Angelina Prima Kurniati Ardian Adam Alfarisyi Artanto Ageng Kurniawan Asri Erbenca Gegeh, Yolando Assyifa, Cynthia Bondan Ari Bowo Bq Desy Hardianti Bq Desy Hardianti Chikal Fachdiana Citra Pangestu Diken Pradana Putra Diska Yunita Eko Darwiyanto Elly Susilowati Emir Septian Sori Dongoran Fachri Pane, Syafrial Fahri Alfiansyah Fauzan, Muhammad Arief Gumilar, Ihshan Hariandi Maulid Harris Febryantony Z Hegar Aryo Dewandaru Hegar Aryo Dewandaru Hetti Hidayati I Gusti Bagus Ady Sutrisna Imelda Atastina J. Ratna Juita S Joshua Tanuraharja Julius Angger Satrio Wicaksono Kadek David Kurniawan Kemas Rahmat Saleh Wibowo Kemas Rahmat Saleh Wiharja Kusuma Handoyo, Amanda Putri Mahendra Dwifebri Purbolaksono Mahmud Dwi Sulistiyo Mira Kania Sabariah Muhammad Agung Agung Muhammad Idris Muhammad Iqbal Muliadi Angga Wicaksono Nunit Prihatoni Siregar Nur Ghaniaviyanto Ramadhan Pangestuaji Widodo, Akhdan Pratami, Rahmat Prawita, Fat'hah Noor Prawita, Fathah Noor Rafie Novianto Sudrajat Ramdana Putra, Haidar Rashid Ria Aniansari Ruhallah, Muhammad Lutfi Shigeru Fujimura Syafrial Fachri Pane, Syafrial Fachri Tedy Gumilang Sejati Warih Maharani Wisnu Riyan Pratama Putra Yudhono, Efwandha Yusuf Basqara, Muhammad