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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
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Sistem Pakar Mendiagnosis Penyakit pada Tanaman Jeruk Menggunakan Metode Dempster Shafer Elsilaturrahmi, Elsilaturrahmi; Kurnia, Fitra; Haerani, Elin; Mai Candra , Reski
Journal of Comprehensive Science Vol. 2 No. 1 (2023): Journal of Comprehensive Science (JCS)
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/jcs.v2i1.224

Abstract

Tanaman jeruk termasuk pada komoditas unggulan nasional yang berperan dalam meningkatkan devisa negara. Namun, berbagai penyakit seringkali menyererang tanaman jeruk sehingga diperlukan suatu sistem untuk mendignosis penyakit pada tanaman jeruk. Penelitian ini bertujuan untuk menganalisis sistem pakar dalam mendiagnosis penyakit pada tanaman jeruk. Penelitian ini menggunakan metode Demster Shafer. Hasil penelitian dapat disimpulkan bahwa dengan penelitian yang dilakukan dapat dipahami konsep dan penerapan dari metode Dempster Shafer dalam mendiagnosis penyakit tanaman jeruk.
Klasifikasi Penyakit Cacar Monyet Menggunakan Metode Support Vector Machine Anugrah, Wendy; Haerani, Elin; Yusra, Yusra; Oktavia, Lola
Journal of Computer System and Informatics (JoSYC) Vol 5 No 3 (2024): May 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i3.5149

Abstract

Monkey pox is a zoonotic disease caused by the monkey pox virus and this disease is very dangerous. Monkey pox can be detected in advance by using information contained in patient data and applying machine learning techniques. This study aims to classify monkey pox using the Support Vector Machine (SVM) method. This test is carried out using a confusion matrix by comparing the ratio of training data and test data with a ratio of 70:30, 80:20, 90:10 and using the RBF kernel. Based on the test results, the highest ratio results were obtained at 90:10 with the best accuracy value of 65% with SVM parameter testing, namely the value C= 10 and y (gamma)= 1. Based on the results of tests carried out using the Support Vector Machine method, the accuracy values ​​were quite good.
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.
Penerapan K-Means Clustering Pada Data Obat/Alkes di Apotik RSUD Selasih Budianita, Elvia; Haerani, Elin; Nazir, Alwis
Seminar Nasional Teknologi Informasi Komunikasi dan Industri 2023: SNTIKI 15
Publisher : UIN Sultan Syarif Kasim Riau

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

Abstract

Apotik merupakan salah satu tempat yang menjual obat-obatan, alat kesehatan (alkes) dan lainnya. Salah satu faktor penting untuk kelangsungan proses jual beli pada apotik yaitu adanya persediaan obat-obatan. Apotik RSUD Selasih sudah memiliki sistem yang menampung data persediaan obat obatan. Sistem tersebut juga memiliki data transaksi penjualan obat/alkes dan data pasien. Namun, persediaan obat-obatan dilakukan hanya dengan memeriksa persediaan obat yang hampir habis kemudian memperbarui stok persediaan obat tersebut sehingga hal ini kurang efisien jika suatu waktu membutuhkan obat dalam jumlah yang besar dan ternyata stok habis. Pada penelitian ini diterapkan suatu metode data mining K-Means Clustering dengan cara menganalisa pada pemakaian obat untuk menghasilkan informasi yang dapat dijadikan sebagai perencanaan dan pengendalian persediaan obat berdasarkan hasil kluster yang terbentuk. Berdasarkan hasil pengujian yang telah dilakukan menggunakan Davies Bouldin Index, diperoleh jumlah kluster terbaik adalah 2 dengan nilai DBI sebesar 0,33 yaitu kluster yang memiliki permintaan yang tinggi dengan penjualan obat selama 12 bulan diatas 3200 buah dan kluster yang memiliki permintaan yang rendah dengan penjualan obat/alkes selama 12 bulan dibawah 3200 buah.
KLASIFIKASI SENTIMEN MASYARAKAT TERHADAP EFISIENSI ANGGARAN PEMERINTAH MENGGUNAKAN METODE NAÏVE BAYES CLASSIFIER Alfaridzy, M. Audi; Haerani, Elin; -, Jasril; Oktavia, Lola
JUTECH : Journal Education and Technology Vol 6, No 1 (2025): JUTECH JUNI
Publisher : STKIP Persada Khatulistiwa Sintang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31932/jutech.v6i1.4969

Abstract

Kebijakan efisiensi anggaran pemerintah Indonesia tahun 2025 merupakan respons terhadap kebutuhan penguatan fiskal dan pengalokasian ulang anggaran untuk program prioritas nasional. Melalui Instruksi Presiden Nomor 1 Tahun 2025, pemerintah menetapkan penghematan sebesar Rp306,7 triliun dengan memotong belanja kementerian/lembaga dan transfer ke daerah. Meskipun ditujukan untuk mendukung program strategis seperti Makan Bergizi Gratis (MBG), kebijakan ini menimbulkan dampak signifikan, seperti pemangkasan anggaran lembaga penting (misalnya BMKG) lebih dari 50%, pembatalan proyek infrastruktur, serta pengurangan tenaga kerja di sektor media publik. Kondisi ini menimbulkan perdebatan di tengah masyarakat terkait kebutuhan penghematan dan potensi risikonya terhadap pelayanan publik, investasi, serta pemerataan pembangunan. Penelitian ini bertujuan mengklasifikasikan sentimen masyarakat terhadap kebijakan efisiensi anggaran berdasarkan komentar dari media sosial Instagram. Tahapan penelitian meliputi pengumpulan data, pelabelan manual, cleaning, case folding, tokenizing, normalisasi, negation handling, stopword removal, stemming, pembobotan TF-IDF, klasifikasi dengan Naïve Bayes, dan pengujian. Sebanyak 1.408 komentar dari dua akun Instagram diklasifikasikan menggunakan metode Naïve Bayes Classifier dengan hasil akurasi 90,74%, presisi 85,16%, recall 98,51%, dan F1-score 91,35%. Penelitian ini diharapkan dapat dikembangkan dengan metode klasifikasi lainnya di masa depan.
Application of ADASYN Technique in Classification of Stroke Disease using Backpropagation Neural Network zikrillah aulia, said rizki; okfalisa, okfalisa; haerani, elin; oktavia, lola
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/jdhv9s39

Abstract

The high prevalence of stroke in Indonesia and the challenge of imbalanced medical record data are major obstacles to the development of an accurate early detection system. This research aims to build a reliable stroke classification model by applying the ADASYN (Adaptive Synthetic Sampling) oversampling technique to address class imbalance before the data is processed using the Backpropagation Neural Network (BPNN) algorithm. The ADASYN technique is applied with the goal of reducing the bias that arises from the imbalanced data distribution between the majority and minority classes. Testing was conducted through various data splitting scenarios (70:30, 80:20, 90:10) and hyperparameter variations to find the optimal configuration. The best results were obtained with the 90:10 data split scheme, using an architecture of 29 neurons and a learning rate of 0.01, which successfully achieved peak performance with an accuracy of 90.46% and an F1-score of 91.03%. This study demonstrates that the combination of ADASYN and BPNN is a highly effective approach for producing a stroke prediction model that is not only accurate but also sensitive to the minority class, thus having great potential as an early detection support tool in the healthcare sector.
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"
Penerapan Algoritma Naïve Bayes Terhadap Klasifikasi Penerima Bantuan Program Keluarga Harapan (PKH) Irsyada, Amelia; Haerani, Elin; Irsyad, Muhammad; Wulandari, Fitri; Afriyanti, Liza
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 2 (2023): Desember 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i2.7203

Abstract

Poverty in Indonesia is one of the complex social issues. As a manifestation of the government's concern about poverty in the country, various assistance programs have been established to target the impoverished population. One such program aimed at alleviating poverty in Indonesia is the Family Hope Program (Program Keluarga Harapan or PKH). PKH is a conditional cash transfer program provided to the impoverished community. The manual selection process for aid recipients is considered less than ideal, leading to issues of improper distribution. In this study, the Naïve Bayes algorithm is applied to classify PKH aid recipients in the Bungaraya Subdistrict, Siak Regency, as part of the government's efforts to tackle poverty. The dataset used consists of 560 records, including data on existing PKH aid recipients and potential recipients from various villages in the Bungaraya Subdistrict for the year 2022. The attributes considered in this research include age, income, number of dependents, dependents attending school, dependents with disabilities, housing status, floor type, and wall type. The highest accuracy obtained through calculations on Google Colab is 99% for an 80:20 ratio, while the accuracy obtained using RapidMiner is 94%.
Pemanfaatan Algoritma K-Means Dalam Menentukan Potensi Hasil Produksi Kelapa Sawit Wahyuni, Ayu Sri; Haerani, Elin; Budianita, Elvia; Afrianti, Liza
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 2 (2023): Desember 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i2.7226

Abstract

Considering the importance of oil palm cultivation now and in the future, as well as the increasing demand for palm oil by the world population, it is necessary to think about efforts to increase the quality and quantity of palm oil production appropriately in order to achieve the desired and achievable goals. Based on data on palm fruit production results from PT Salim Ivomas Pratama Tbk, it can be seen that fruit production varies in several places. The potential yield of oil palm fruit is based on the harvested area, actual production and year of planting. K-Means welding can help identify the potential of oil palm, with results that vary from day to day. This process allows locations with similar production patterns, which facilitates management decisions and production strategies. In this research, potential fruit planting areas were grouped using the K-Means algorithm. K-Means aims to facilitate the grouping of blocks with high and low fruit production. The data used is 180 data for the last 5 years, namely from 2018 to 2022, with the attributes Harvest Block, Area, Sheet Weight, and Product Realization or quantity. This research uses the help of Rapidminer and Google Colab software. The results of this research show that C1 (the highest) is 125 Harvest Block data in the sense that the first group is included in the good or high harvest yield category in 2018-2022, and C0 (the lowest) is 55 Harvest Block data in the sense that the second group is included low yield category 2018-2022.
Klasifikasi Sentimen Presepsi Masyarakat di Instagram Terhadap Paslon Pilpres 2024 Menggunakan Naïve Bayes Classifier (NBC) Akbar, Lionita Asa; Haerani, Elin; Syafria, Fadhilah; Nazir, Alwis; Budianita, Elvia
Jurnal Komtika (Komputasi dan Informatika) Vol 8 No 1 (2024)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v8i1.11293

Abstract

The 2024 presidential election has attracted considerable attention as it has become a controversial issue among the public. Various positive and negative opinions generated can potentially turn into rumors. One of the means used by the public to express their opinions is the social media platform Instagram. Data on public opinions on Instagram can be processed into valuable information through sentiment classification. This research conducted sentiment classification on public perceptions towards the 2024 presidential candidates using a naïve Bayes classifier. The study utilized a dataset consisting of 1000 comments. These comments were collected from several posts on the social media platform Instagram discussing the presidential and vice-presidential candidates. The comments were manually labeled by an expert who is a lecturer in the Indonesian language. Classification was carried out after preprocessing and weighting TF-IDF stages. Based on the research findings, the naïve Bayes classifier method showed an accuracy of 82% and an F1-Score of 83.93% obtained from a 90%:10% split of training and testing data. These results indicate that the naïve Bayes classifier method is effective in classifying the sentiments of the public on Instagram towards the 2024 presidential candidates.
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