Article Per Year (5 Year)
p-Index From 2020 - 2025
7.007
P-Index
This Author published in this journals
All Journal BAHASA DAN SASTRA Toleransi: Media Ilmiah komunikasi Umat Beragama JURNAL PENDIDIKAN FISIKA JURNAL KEPEMIMPINAN DAN PENGURUSAN SEKOLAH Jurnal Obsesi: Jurnal Pendidikan Anak Usia Dini Jurnal JUMPED (Jurnal Manajemen Pendidikan) JURNAL PENDIDIKAN TAMBUSAI Jurnal Literasiologi JISIP: Jurnal Ilmu Sosial dan Pendidikan HIKMAH : JURNAL PENDIDIKAN AGAMA ISLAM Idaarah: Jurnal Manajemen Pendidikan Akademika : Jurnal Keagamaan dan Pendidikan Jurnal Ekonomi Manajemen Sistem Informasi Jatilima : Jurnal Multimedia Dan Teknologi Informasi Jurnal Sistem Komputer dan Informatika (JSON) Journal of Computer Networks, Architecture and High Performance Computing Jurnal Ilmu Manajemen Terapan (JIMT) Jurnal Manajemen Pendidikan Al Hadi Instal : Jurnal Komputer ALACRITY : Journal Of Education Jurnal Masyarakat Indonesia Prioritas : Jurnal Pengabdian Kepada Masyarakat Jurnal Ilmu Multidisplin Jurnal Al-Kifayah: Ilmu Tarbiyah dan Keguruan Jurnal Ilmiah Pendidikan dan Keislaman Innovative: Journal Of Social Science Research Sharing: Journal Of Islamic Economics, Management And Business ALENA : Journal of Elementary Education Journal of Islamic Education and Ethics QOSIM: Jurnal Pendidikan, Sosial & Humaniora NIZAMUL ILMI: Jurnal Manajemen Pendidikan Islam Proceeding Of International Conference On Education, Society And Humanity
Tuti Andriani
Fakultas Tarbiyah dan Keguruan Universitas Islam Negeri Sultan Syarif Kasim Riau
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry 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 Library & Information Science Mathematics Physics Public Health Social Sciences Other

Published : 56 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search
Journal : Instal : Jurnal Komputer

 1 
Expert System for Diagnosing Gastric Disease Using the Forward Chaining Method Andriani, Tuti; Mentari, Risca Sri; Situkkir, Meiarni; Nasution, Darmeli
Bahasa Indonesia Vol 16 No 02 (2024): Instal : Jurnal Komputer
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jurnalinstall.v16i02.226

Abstract

Health is the most crucial aspect of life, as it signifies a state of well-being and vitality. Gastric diseases pose a significant health concern, often requiring prompt and accurate diagnosis for effective treatment. In this study, an expert system utilizing the forward chaining method is developed to aid in diagnosing gastric diseases. The system aims to streamline the diagnostic process, providing users with timely and reliable results to facilitate early intervention and management of stomach disorders. Through the integration of technology and medical expertise, this system offers a valuable tool in enhancing healthcare delivery and improving patient outcomes in the realm of gastric health. The research findings highlight the effectiveness of an expert system employing the forward chaining method in diagnosing gastric diseases. The system demonstrates the capability to efficiently identify and assess symptoms related to stomach disorders, offering users a reliable and accessible platform for obtaining accurate diagnostic results. By leveraging current knowledge base rules and user-provided symptom data, the system enhances the diagnostic process, potentially reducing both time and costs associated with diagnosing gastric diseases. This study underscores the significance of technological advancements in healthcare, particularly in the realm of gastric health, by providing a valuable tool for early detection and management of stomach disorders.
Implementation of Clustering Using Representative (CURE) Method for Segmenting Telephone Call Behavior as a Basis for Policy Making by Telecommunication Providers Andriani, Tuti
Bahasa Indonesia Vol 16 No 05 (2024): Instal : Jurnal Komputer
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jurnalinstall.v16i05.318

Abstract

The quality of service provided by telecommunication providers is critical to ensure competitiveness in a competitive market. This quality often depends on the policies taken by the provider, which must be based on factual data to avoid decisions that deviate from field conditions. One resource that can be utilized is customer phone call data, which can be analyzed and grouped to understand call behavior. This study aims to implement the Clustering Using Representative (CURE) method in segmenting phone call behavior. The CURE method was chosen because of its ability to find clusters of various shapes and sizes, even when key attributes have low contributions. The test results show that this method is able to produce clusters with a high average percentage of accuracy. Thus, the CURE method is proven to be effective for segmenting phone call behavior, providing a strong foundation for telecommunication providers to make strategic decisions, such as tariff adjustments, loyalty programs, and customer retention strategies.
Methods for Using Tracking Trees to Determine an Ant's Path on a Cartesian Plane via Breadth-First Search Khairul; Andriani, Tuti; Mentari, Risca Sri
Bahasa Indonesia Vol 15 No 02 (2023): Instal : Jurnal Komputer Periode (Juli-Desember)
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jurnalkomputer.v15i02.153

Abstract

Because scientific knowledge is expanding at such a dizzying rate, it is essential that society keeps up with technological trends. It is clear from the evolution of this technology that the utilisation of computers as a means to facilitate work and enhance efficiency and effectiveness is also on the rise. Here we see an issue with Artificial Intelligence (AI), a subfield of computer science concerned with teaching computers to mimic human intelligence. When first developed, computers were mostly used for calculations. But computers are becoming more and more integral to human existence. Computers are now expected to be capable of performing any task that a human being can. Their original purpose has expanded beyond simple calculation. Because of our vast store of information and vast experience, we are capable of fixing any problem that arises. One gets more knowledgeable as one learns. An individual's problem-solving abilities are directly proportional to their level of knowledge. The ability to reason, to derive conclusions from one's own knowledge and experience, is an essential complement to one's level of knowledge
Comparison of Standard and Squeeze-and-Excitation Enhanced DenseNet Architectures for Tomato Leaf Disease Classification Using Data Augmentation Andriani, Tuti; Nainggolan, Irfan
Bahasa Indonesia Vol 17 No 08 (2025): Instal : Jurnal Komputer
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jurnalinstall.v17i08.429

Abstract

The advancement of deep learning has significantly improved the automation of plant disease detection through image classification. This study compares the performance of standard DenseNet121 and an enhanced version incorporating Squeeze-and-Excitation (SE) blocks for classifying tomato leaf diseases. A dataset derived from PlantVillage was used, covering multiple disease categories and healthy leaves. To improve generalization, extensive data augmentation techniques were applied. Both architectures were implemented and trained using PyTorch, with evaluation metrics including accuracy, precision, recall, F1-score, and inference time. The experimental results demonstrate that DenseNet121-SE significantly outperforms the standard DenseNet121, achieving a classification accuracy of 99.00%. The integration of SE blocks allows the model to recalibrate channel-wise features adaptively, enhancing sensitivity to important patterns while maintaining computational efficiency. This study highlights the effectiveness of attention mechanisms and data augmentation in improving classification performance and supports their practical application in intelligent agriculture systems.
Co-Authors Abd Samad, Abd Abdul Ghoni Adelin, Muhammad Aflizah, Nur Afriza Afriza Afriza, Afriza AHMAD ANSORI ARDIANSYAH ARDIANSYAH Ardiansyah, Adriansyah Atlis, Linda Dea Attoillah, Muhammad Farhan Bangun, Gladis Salsabilla Barus, Apriwati bin Jamaludin, Muhammad Arif Sufyan Cahyono, Imam Dalillah, Aupi Damsir, Damsir Darmeli Nasution Dinata, Yuriyan Faujiah, Syifa Febrian, Vicky Rizki Gita Morinda, Claudio Hafis, Gustianto Nur Hestivik, Chelsi Husni Thamrin Ilham Wahyudi Ilham, Rahmat Intan Sari, Arrum Iriani, Umi Jumrotun, Siti Khairul Lukman Hakim Lyadi, Muslim M. FAISAL AKBAR Matias Julyus Fika Sirait Mentari, Risca Sri Morinda, Claudio Gita Mufid, Dyonel Ilham Muhammad Amin Muhammad Iqbal Muhammad Iqbal Muhammad Syaifuddin Muhammad Syaifuddin Muhammad Syaifudin Muslim Afandi Muti’ah, Siti Nainggolan, Irfan Nasution, Darmeli Nini Aryani Nur Azizah Nuraini Nuraini Nurhafizah Nurhafizah Nurlina Nurlina PURNAMA, ADE Putra, Alif Yunanda Putra, Rezi Muda Putra, Suntama Putri, Salsabillah Ramadhan, Muhammad Aryo Rizka Damayanti salam, Indra Agus Salim, Muhammad Samsul Arifin Santika, Mira Saputri, Eki Nining Septiani, Iga Shihabuddin, Ahmad Simangunsong, Pandi Barita Nauli Sitorus, Syahrial Situkkir, Meiarni SRI RAHAYU Sukaiman Sukma, Arif Bahtera Syafiuddin, Fauzan Azima Syafuddin, Muhammad Syahril Syarif Tarigan, Sry Wahyana Br Tasbih, M. Irfan Umar Faruq Yanti, Annisa Darma Yudistira, Srikandi Yulaekah, Yulaekah Yuniati Yuniati Zein, M. Rasyad Zuhairansyah Arifin Zuhairansyah, Zuhairansyah