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All Journal TEKNIK INFORMATIKA Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Sistemasi: Jurnal Sistem Informasi Seminar Nasional Teknologi Informasi Komunikasi dan Industri Krea-TIF: Jurnal Teknik Informatika Building of Informatics, Technology and Science Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Computer Science and Information Technology (CoSciTech) Bulletin of Computer Science Research JUTECH : Journal Education and Technology Journal of Comprehensive Science Knowbase : International Journal of Knowledge in Database Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Teknik Indonesia Indonesian Journal of Innovation Multidisipliner Research Indonesian Journal of Innovation Multidisipliner Research INOVTEK Polbeng - Seri Informatika Jurnal Komtika (Komputasi dan Informatika)
Elin Haerani
UIN Suska Riau
Religion Aerospace Engineering Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Transportation Veterinary Other

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PENGEMBANGAN SISTEM PENDUKUNG KEPUTUSAN PENENTUAN KONSENTRASI BIDANG KEAHLIAN MAHASISWA DENGAN INTEREST INVENTORY Haerani, Elin; Rukun, Kasman; Rizal, Fahmi
JURNAL TEKNIK INFORMATIKA Vol 13, No 1 (2020): JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (553.916 KB) | DOI: 10.15408/jti.v13i1.15710

Abstract

Universities are designed to prepare graduates who are ready to enter the workforce and are able to develop a professional attitude. Educational institutions such as the University need a form of decisions in determining the right concentration for students, so that the learning process can be achieved well. The decision is very influential on the process of handling the choice of alternative concentration, choosing an appropriate concentration of interest will also have an impact on the research focus for the final assignment of students. This research develops student concentration selection system in Electrical. Currently the concentration determination system is based only on academic assessment alone, regardless of student interest, so that it can impact on student learning outcomes. The system was developed by combining academic judgment and interest inventory with three criteria, ie, interest tests using interest inventory, prerequisite concentration course grades, and GPA. The system is built using an intelligent system model that is Fuzzy Multiple Attribute Decision Making (FMADM), which helps the Department in the selection process and helps the process of career guidance on students. With this selection system, the Department can be provide the most suitable concentration decisions with interest in student.
Sistem Pendukung Keputusan untuk Rekomendasi Pemilihan Guru Terbaik Menggunakan Metode Simple Additive Weighting Sapitri, Janaria; Vitriani, Yelfi; Haerani, Elin; Kurnia, Fitra
Indonesian Journal of Innovation Multidisipliner Research Vol. 2 No. 2 (2024): June
Publisher : Institute of Advanced Knowledge and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69693/ijim.v2i2.139

Abstract

Guru yang profesional dibutuhkan di sekolah-sekolah, seperti SMKN Kehutanan Pekanbaru, untuk memberikan pengalaman belajar mengajar yang unggul. Oleh karena itu, sekolah terus berupaya untuk meningkatkan kualitas guru dengan menilai bagaimana para pengajar menjalankan tugasnya untuk memastikan mereka memenuhi kriteria kompetensi. Sistem pendukung keputusan adalah sistem yang dapat memecahkan masalah dan menanganinya tujuannya bukan untuk menggantikan pengambil dilanjutkan dengan prosedur perangkingan untuk menemukan alternatif terbaik dari daftar pilihan keputusan, melainkan untuk membantu merekomendasikan pengambil keputusan. Simple Additive Weighting (SAW) adalah metode yang populer dalam sistem pendukung keputusan karena dapat menetapkan nilai pembobotan untuk setiap fitur dan kemudian yang tersedia. Pada contoh kasus ini, metode SAW (simple additive weighting) yang digunakan untuk memilih guru terbaik di SMKN Kehutanan Pekanbaru berhasil membantu pengguna, dan diperoleh rekomendasi guru terbaik yaitu A12 dengan nilai akhir 0,95. Berdasarkan pengujian UAT, diperoleh hasil 85% yang menandakan bahwa aplikasi ini dapat diterima dengan baik oleh pengguna.
Klasifikasi American Sign Language Menggunakan Convolutional Neural Network Israldi, Tino; Haerani, Elin; Sanjaya, Suwanto; Syafria, Fadhilah
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2570

Abstract

Communicating is a necessity for all groups or individual because each individual should communicate with their surroundings. Communicating can also make us get information so that it can be used as a reference to be able to adapt. Verbal language used by speaking out loud is a way of communicating with individuals, but not all individuals can communicate with it, especially there are some individuals who have hearing limitations. Because of these limitations, another program that can be used is through sign language. Language requirements are languages that are usually used by people with disabilities in terms of hearing or speaking and sign language also has a fairly well-known sign language standard, namely the American Sign Language (ASL) standard. Unlike languages in the world, sign language is also often of little interest to most people because people's interest in sign language is still lacking so that most people are unable to understand their language. Sign language has many types, one of which is sign language by using hands to form letters and numbers. In overcoming these problems, the solution is to create a system that can be used to recognize sign language, the system developed is a system that used machine learning technology. This study will propose an ASL classification approach through data preprocessing and a convolutional neural network model. The proposed model can classify ASL hand posture images to be translated into the alphabet. The result of this study is an model with accuracy of 99.8% obtained from the process of merging preprocessing data and the convolutional neural network model.
S Sistem Pakar Diagnosa Gangguan Kejiwaan Menggunakan Metode Inferensi Forward Chaining dan Certainty Factor Fauzan, Muhammad; Wulandari, Fitri; Haerani, Elin; Oktavia, Lola
Building of Informatics, Technology and Science (BITS) Vol 4 No 4 (2023): March 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i4.3232

Abstract

The era of artificial intelligence AI technology is now an advantage because the system does all the work according to the human brain. Expert Systemis abranch ofartificial intelligencethat adapts the mind and reasoning of an expert to solve a problem and make a decision so that it draws conclusions based on the facts. From cases of psychiatric disorders, this expert system is highly recommended to make it easier to find out what type of disorder you are suffering from to assist the public and experts in diagnosing diseases quickly and accurately. For this reason, researcherscreated an expert system for diagnosingpsychiatric disordersusing the forwardchaining inferencemethod and certainty factor. Based on the results of the implementation and analysis thathave been carried out in this study, it produces a software system, namely an expert system that has an easy-to-understand display, and can assist experts in diagnosing psychiatric disorders
Sistem Klasifikasi Penyakit Jantung Menggunakan Teknik Pendekatan SMOTE Pada Algoritma Modified K-Nearest Neighbor Novitasari, Fitria; Haerani, Elin; Nazir, Alwis; Jasril, Jasril; Insani, Fitri
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3610

Abstract

The heart is a vital organ that plays a crucial role in pumping oxygenated blood and nutrients throughout the body. Heart disease refers to damage to the heart that can occur in various forms, caused by infections or congenital abnormalities. The World Health Organization (WHO) reports nearly 17.9 million deaths each year due to heart disease. In Indonesia, the prevalence of heart disease is around 1.5%, meaning that in 2018, approximately 15 out of 1,000 people, or nearly 2,784,060 individuals, were affected by this disease, according to the Basic Health Research data (Riskesdas) 2018. Many people have limited knowledge about heart health, leading to a lack of awareness of their heart conditions. This can be attributed to a lack of understanding regarding the importance of medical checkups related to heart health. Modified K-Nearest Neighbors (MKNN) is one of the data mining methods applied for classifying the risk of heart disease. The research utilized data obtained from the UCI dataset repository, which consists of 918 records with 12 attributes. To balance the imbalanced dataset with minority classes, the Synthetic Minority Over-sampling Technique (SMOTE) approach was used to generate new synthetic samples from the minority class. The objective of developing a web-based system for heart disease classification is to assist the public in assessing their risk of heart disease as early as possible, enabling them to take preventive actions sooner. The accuracy results of the MKNN algorithm with a 90:10 ratio are 80.37%, while with the MKNN+SMOTE approach, the accuracy increased to 84.00%. The use of the SMOTE approach improved the accuracy of low-performing data.
Implementation of Telegram Chatbot as Information Service of Madani Hospital Pekanbaru hariansyah, Aldi; Haerani, Elin; Novriyanto, Novriyanto; Affandes, Muhammad
Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Vol 11 No 3 (2023): Vol. 11, No. 3, December 2023
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JIM.2023.v11.i03.p05

Abstract

very institution or organization aims to create a positive image through information services. In Indonesia, many hospitals provide information through telephone or official websites. However, Madani Hospital in Pekanbaru requires improvement. This research developed a registration chatbot on Telegram, making it easier for patients to make doctor appointments, view schedules, and access important information. Telegram was chosen for being lightweight, fast, and popular. The research involved literature surveys, problem identification, literature review, and data collection. The results were used to design the chatbot flow. The system was developed using Sommerville's Waterfall method, covering requirement definition, design, implementation, testing, integration, operation, and improvement. User Acceptability Testing is a key stage in implementation. User Acceptability Testing questionnaires were distributed to 20 prospective patients with various questions. The chatbot implementation used the Python API for Telegram and a MySQL database, with Black Box testing covering patient access, registration, functions, admin authentication, create update read delete admin, and error handling. The results of User Acceptability Testing showed an accuracy achievement of 77.8%, which means it is Very Good.
Implementasi Algoritma Convolutional Neural Network (Resnet-50) untuk Klasifikasi Kanker Kulit Benign dan Malignant: Implementation of Convolutional Neural Network Algorithm (ResNet-50) for Benign and Malignant Skin Cancer Classification Gusti, Gogor Putra Hafi Puja; Haerani, Elin; Syafria, Fadhillah; Yanto, Febi; Gusti, Siska Kurnia
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 3 (2024): MALCOM July 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i3.1398

Abstract

Kulit sebagai organ terluar yang menutupi seluruh bagian tubuh manusia rentan terhadap berbagi penyakit, salah satunya kanker kulit. Penggunaan teknologi malignant, khususnya Convolutional Neural Network (CNN) diangkat menjadi topik penelitian karena kemampuan CNN untuk secara otomatis mengenali fitur penting dalam klasifikasi citra medis kanker kulit. Oleh karena itu dilakukan penelitian pengklasifikasian penyakit kanker kulit benign (jinak) dan malignant (ganas) menggunakan algoritma CNN arsitektur ResNet-50 dengan dataset berupa 5000 data latih kanker kulit benign dan 4600 data latih kanker kulit malignant.Model CNN yang telah dirancang dengan epoch 50 menggunakan optimizer Adam dan batch size sebesar 54 serta melibatkan beberapa teknik augmentasi data guna meningkatkan keragaman dataset untuk kemudian model hasil perancangan diimplementasikan ke dalam tampilan sebuah website dengan menggunakan Flask sebagai kerangka kerja yang menghubungkan antara model deep learning dan website agar bisa diakses oleh pengguna. Metode pengujian blackbox dilakukan demi memastikan sistem dapat melakukan klasifikasi kanker kulit melalui input berupa citra medis kedalam 2 kelas yaitu benign dan malignant dengan baik serta didapatkan hasil akurasi model sebesar 94,88 % dan loss sebesar 13,24%.
Penggunaan Model Bahasa indoBERT pada metode Random Forest untuk Klasifikasi Sentimen dengan Dataset Terbatas Pranata, Joni; Agustian, Surya; Jasril, Jasril; Haerani, Elin
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6335

Abstract

Masalah keterbatasan data latih menjadi tantangan utama dalam klasifikasi sentimen di berbagai bahasa, termasuk bahasa Indonesia, terutama untuk analisis sentimen terkait topik tertentu. Hal ini disebabkan oleh berbagai faktor, dan umumnya adalah kebutuhan untuk mengetahui dengan segera bagaimana sentimen terhadap suatu isu, sehingga tidak mungkin menghabiskan waktu untuk memberi label yang cukup pada data untuk proses pelatihan. Penelitian ini mengusulkan model klasifikasi sentimen dengan sumber data pelatihan yang sedikit, pada studi kasus pengangkatan Kaesang Pangarep sebagai ketua umum PSI. Algoritma Random Forest digunakan sebagai model dasar (baseline) yang dioptimasi dengan penambahan data eksternal untuk training, pemrosesan teks (text preprocessing) dan parameter tuning. Fitur input yang digunakan adalah model bahasa IndoBERT sebagai embedding kata untuk menghasilkan representasi teks yang lebih kontekstual. Hasil penelitian menunjukkan bahwa metode IndoBERT dengan Random Forest yang dioptimasi memberikan peningkatan performa yang signifikan dibandingkan baseline, sebesar 6%. Hasil klasifikasi model yang paling optimal sebesar 54% unutk F1-score dan 63% akurasi. Temuan ini menegaskan bahwa penambahan data eksternal dan optimasi parameter dapat meningkatkan kemampuan generalisasi model dalam klasifikasi sentimen bahasa Indonesia. Penelitian ini diharapkan dapat menjadi referensi metodologis bagi studi klasifikasi sentimen serupa yang menghadapi kendala ukuran dataset.
Pengembangan Sistem Informasi Cerdas Career Guidance Berbasis Minat Di Perguruan Tinggi Haerani, Elin; Syafria, Fadhilah; Muhammad Yusuf Fadhillah
Computer Science and Information Technology Vol 5 No 3 (2024): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

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

Abstract

The development of an intelligent information system career guidance based on interests is designed to address the difficulties faced by high school graduates in determining their major in higher education. Difficulties often arise due to confusion caused by a lack of guidance and information. To help address this issue, an intelligent interest- based major recommendation information system was developed using the breadth-first search (BFS) method, which consists of 8 interest categories based on the Rothwell-Miller Interest Blank theory (RMIB). This intelligent information system can provide direction and guidance (career guidance) to students in determining the right major according to their interests. Career guidance application services are highly needed by students who are currently in their unstable teenage years, struggling to determine their paths. This system generates output in the form of major recommendations along with related information. The system is built with PHP and MySQL and tested using the user acceptance test (UAT).
Application of Data Mining for Ceramic Sales Data Association Using Apriori Algorithm Habibi, M. Ilham; Nazir, Alwis; Haerani, Elin; Budianita, Elvia
Knowbase : International Journal of Knowledge in Database Vol. 4 No. 2 (2024): December 2024
Publisher : Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30983/knowbase.v5i2.8757

Abstract

This research is conducted to provide an understanding of consumer purchasing patterns at CV. Sukses Bersama by applying data mining using the association rules method and the Apriori algorithm to identify the relationships between one item that influences other items within a ceramic sales dataset at CV. Sukses Bersama. This information is expected to serve as a foundation for improving sales strategies, optimizing customer satisfaction, and expanding the company's market share. The Apriori algorithm is a popular algorithm implemented to identify association rules in data mining. The Apriori algorithm was chosen due to its ability to efficiently identify association rules and its good scalability in handling large datasets. This research begins with the collection of ceramic sales data, followed by data preprocessing to clean and prepare the data. The Apriori algorithm is then applied to discover the association rules, which generate two matrices: support and confidence, and the results are subsequently evaluated. This research was conducted using Google Colaboratory, a web application that is a cloud-based platform provided by Google to run Python code. The results of the study show that the Apriori algorithm can depict significant association structures between different ceramic brand types in the sales data of CV. Sukses Bersama. The calculation results show that the rule has the maximum support and confidence value, namely 67% support value and 84% confidence value in the rule "if you buy the DIAMD brand, you will buy the TOTAL brand"
Co-Authors -, Jasril Abdul Haris Kurnia Sandi Harahap Afrianti, Liza Afriyanti, Liza Akbar, Lionita Asa Alfaridzy, M. Audi Amelia, Felina Ananda, Rahmadan Alam Ardan Anugrah, Wendy Baehaqi Cahyadi, Wahyu Dede Fadillah Elsilaturrahmi, Elsilaturrahmi Elvia Budianita Fadhilah Syafria Fahmi Rizal Febi Yanto Fitri Insani Fitri Wulandari Fitria Novitasari Gusti, Gogor Putra Hafi Puja Gusti, Siska Kurnia Habibi, M. Ilham hariansyah, Aldi Ikhsan, Tomi Indrizal, Habibi Putra Irsyada, Amelia Israldi, Tino Iwan Iskandar Jasril Jasril Kasman Rukun kurnia, fitra Mai Candra , Reski Maulani, Tedi Muhammad Affandes Muhammad fauzan Muhammad Fikry Muhammad Irsyad Muhammad Taufiq Muhammad Yusuf Fadhillah Muhammad, Raja Allifin Nazir, Alwis Novriyanto Novriyanto Okfalisa Okfalisa Okfalisa Oktavia, Lola Prananda, Alga Pranata, Joni Rahman, Muhammad Taufikur Ramadhani, Siti Rendi Ibnu Syahputra Sapitri, Janaria Surya Agustian Suwanto Sanjaya Syafria, Fadhillah Syah Utama, Isruel Vitriani, Yelfi Vitriani, Yelvi Wahyuni, Ayu Sri Yusra, Yusra zikrillah aulia, said rizki