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All Journal Telematika : Jurnal Informatika dan Teknologi Informasi Sistemasi: Jurnal Sistem Informasi Jurnal CoreIT Jurnal Informatika Universitas Pamulang Krea-TIF: Jurnal Teknik Informatika JOISIE (Journal Of Information Systems And Informatics Engineering) Building of Informatics, Technology and Science Zonasi: Jurnal Sistem Informasi Jurnal Daur Lingkungan Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Computer Science and Information Technology (CoSciTech) AloHA International Journal of Health Advancement (AIJHA) Journal of Community Development JURNAL ILMU DAN TEKNOLOGI KESEHATAN TERPADU Malcom: Indonesian Journal of Machine Learning and Computer Science JURNAL SANITASI LINGKUNGAN Journal Of Artificial Intelligence And Software Engineering SEGANTANG LADA : JURNAL PENGABDIAN KESEHATAN Jurnal Indonesia : Manajemen Informatika dan Komunikasi Science, Technology, and Communication Journal
Iwan Iskandar
Jurusan Kesehatan Lingkungan, Poltekkes Kemenkes, Tanjungpinang, Kepulauan Riau,, 29124
Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Electrical & Electronics Engineering Engineering Environmental Science Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Medicine & Pharmacology Nursing Public Health Social Sciences

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Klasifikasi Sentimen Menggunakan Metode Multilayer Perceptron dengan Fitur TF-IDF: Sentiment Classification Using Multilayer Perceptron Algorithm with TF-IDF Features Arasy, Abdurrahman; Agustian, Surya; Handayani, Lestari; Iskandar, Iwan
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 3 (2025): MALCOM July 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i3.2052

Abstract

Media sosial, khususnya Twitter (X), telah menjadi platform utama dalam diskusi politik dan kebijakan pemerintah. Istilah dalam pengiriman pesan pada Twitter dikenal sebagai Tweet yang terdiri dari pesan dengan maksimal 280 karakter. Meskipun Tweet seringkali hanya berupateks, juga dapat menyertakan hyperlink, video, dan jenis media lainnya yang dapat digunakan untuk mengukur opini publik. penelitian ini bertujuan mengklasifikasikan sentimen masyarakat terkait pengangkatan Kaesang Pangarep sebagai Ketua Umum Partai Solidaritas Indonesia (PSI) dengan metode Multi-Layer Perceptron (MLP) Classifier dengan pendekatan Term Frequency-Inverse Document Frequency (TF-IDF) menggunakan bahasa pemograman python. Data yang digunakan terdiri dari 300 tweet, dengan 100 tweet perkelas atau opsi untuk hasil yang optimal. Tiga kategori tersebut adalah positif, netral, dan negatif. Berdasarkan penelitian yang telah dilakukan metode terbaik mencapai F1-score sebesar 0,6767 dan akurasi 0,6667. Hasil ini menunjukkan bahwa kombinasi MLP Classifier dan TF-IDF dapat mengatasi keterbatasan dataset hingga tingkat tertentu dibandingkan metode baseline. Penelitian ini juga memberikan wawasan tentang optimasi klasifikasi sentimen dalam kondisi data terbatas, yang dapat diterapkan pada topik lain dengan permasalahan serupa
Clustering Keluarga Miskin Desa Bina Baru dengan Metode K-Medoids Amelia, Felina; Iskandar, Iwan; Gusti, Siska Kurnia; Haerani, Elin; Yusra, Yusra
Krea-TIF: Jurnal Teknik Informatika Vol 11 No 1 (2023)
Publisher : Fakultas Teknik dan Sains, Universitas Ibn Khaldun Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32832/krea-tif.v11i1.14104

Abstract

Kemiskinan di Indonesia terjadi di berbagai daerah, mulai pedesaan hingga perkotaan memiliki permasalahan kemiskinan masing – masing. Masalah kemiskinan juga dialami oleh Desa Bina Baru. Desa Bina Baru yang memiliki jumlah penduduk sebanyak 5.760 jiwa dengan total 1.742 keluarga, yang tersebar dalam 30 Rukun Tetangga (RT) dan 8 Rukun Warga (RW). Upaya dalam penurunan angka kemiskinan dapat dilakukan dengan berbagai cara, mulai pembangunan yang merata, penyaluran bantuan yang tepat sasaran, pemberian kebijakan yang tepat, dan lain sebagainya. Pengelompokan kemiskinan menjadikan salah satu upaya untuk menurunkan angka kemiskinan agar dapat memberikan informasi kepada pemerintahan daerah dalam memberikan kebijakan yang lebih tepat guna. Clustering merupakan teknik data mining yang bertujuan untuk mengelompokkan objek-objek data menjadi beberapa Cluster. Pada penelitian ini pengelompokkan dilakukan dengan teknik pengolahan data mining dengan algoritme K-Medoids dari data Desa Bina Baru tahun 2020 berjumlah 1.005. Hasil perbandingan perhitungan untuk Cluster 1 (kaya) sebanyak 527 penduduk, Cluster 2 (menengah) sebanyak 248 penduduk, dan Cluster 3 (miskin) sebanyak 225 penduduk, Hasil evaluasi dari algoritme k-Medoids adalah 0,991 yang menunjukan cluster yang dibentuk memberikan pengelompokan informasi yang baik. Hasil pengelompokan ini dapat dijadikan acuan untuk informasi kelompok keluarga miskin yang diperlukan pemerintah agar bantuan yang diberikan tepat sasaran.
Bising di Area, Risiko di Telinga: Studi Multi-Level Kebisingan di Lingkungan Kerja Unit Usaha Mebel Kota Tanjungpinang MF, M.Yusuf; Purnama, Irgi; Idris, M. Fadhil; Iskandar, Iwan
Jurnal Daur Lingkungan Vol 8, No 2 (2025): Agustus
Publisher : Universitas Batanghari Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33087/daurling.v8i2.422

Abstract

Noise is one of the important risk factors in the workplace that is often overlooked, particularly in small-scale furniture enterprises that have limitations in controlling the work environment. High noise exposure has the potential to cause hearing disorders as well as non-audiological complaints that impact worker productivity. This study aimed to analyze noise levels using a multi-level approach at the sound source, workers, supporting areas, and surrounding environment, and to examine their relevance to worker characteristics. The study was conducted in furniture enterprises in Tanjungpinang City with a quantitative descriptive design, using direct noise measurements in accordance with the Ministry of Manpower Regulation No. 5 of 2018 and a worker characteristics questionnaire. The findings showed that 78% of workers were exposed to noise exceeding the Threshold Limit Value (TLV), with the majority being in the productive age group (59%) and having more than two years of work experience (69%). A total of 31% of workers reported hearing complaints and 63% experienced non-audiological symptoms, while more than half of the workers (53%) did not use personal protective equipment (PPE). Multi-level analysis revealed that noise originated not only from machines but was also influenced by workspace layout, worker behavior, and the lack of environmental protection systems. In conclusion, noise control requires a comprehensive strategy through workspace layout adjustments, engineering controls on machines, increased compliance with PPE use, and protection of the surrounding environment. The contribution of this study emphasizes the urgency of a multi-level approach in protecting worker health, improving productivity, and supporting the sustainability of furniture enterprises. Keywords : Occupational Health and Safety; Occupational Noise; Multi-level Approach; Furniture Workers; Tanjungpinang
KLASIFIKASI PENYAKIT TANAMAN PADI MENGGUNAKAN ARSITEKTUR DENSENET-121 DAN AUGMENTASI DATA Yanto, Febi; Agustina, Auliyah; Budianita, Elvia; Iskandar, Iwan; Syafria, Fadhilah
JOISIE (Journal Of Information Systems And Informatics Engineering) Vol 8 No 1 (2024)
Publisher : Institut Bisnis dan Teknologi Pelita Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35145/joisie.v8i1.4256

Abstract

Padi (Oryza sativa) merupakan salah satu jenis tanaman pangan dimana beras sebagai hasil tanaman padi, menjadi bahan pangan utama untuk sebagian besar penduduk indonesia. Dalam proses budidaya padi, tantangan penyakit seringkali menjadi ancaman yang signifikan. Menyebarnya penyakit menyebabkan penurunan ekonomi, seperti pada tahun 2023 penurunan 0,22%. Selain itu minimnya pengetahuan dan wawasan petani dalam mengidentifikasi dan mendiagnosa jenis penyakit padi menjadi penyebab kurangnya hasil produksi padi. Oleh karena itu perlu adanya suatu klasifikasi penyakit padi menggunakan DenseNet-121 dan augmentasi data. Penelitian ini menggunakan pendekatan deep learning yakni Convolutional Neural Network (CNN) dengan arsitektur DenseNet-121 dan augmentasis data crop. DenseNet saat ini banyak digunakan untuk klasifikasi, DenseNet memanfaatkan koneksi padat antar lapisan, mengurangi jumlah parameter, memperkuat propagasi, dan mendorong pemanfaatan kembali fitur. Menggunakan dataset yang berasal dari situs Kaggle yang terdiri dari 3 jenis penyakit tanaman padi yaitu brown spot, blast, dan blihgt dengan setiap kelas terdiri dari 250 citra sehingga semua data berjumlah 750 citra. Hasil terbaik dari beberapa pengujian diperoleh akurasi terbaik sebesar 99,17% dan los 0,0355 menggunakan model DenseNEt-121, pembagian data 90;10 dengan menggunakan leraning rate 0,001 dan dropout 0,01 serta menggunakan augmentasi data, sedangkan untuk hasil akurasi tanpa augmentasi diperoleh hasil akurasi terbaik yaitu 95,00%dengan pembagian data 90;10, learning rate 0,01 dan dropuot 0,1.
A classification of Quran translations using K-nearest neighbors, support vector machine and random forest method Delifah, Nur; Harahap, Nazruddin Safaat; Agustian, Surya; Irsyad, Muhammad; Iskandar, Iwan
Science, Technology, and Communication Journal Vol. 6 No. 1 (2025): SINTECHCOM Journal (October 2025)
Publisher : Lembaga Studi Pendidikan dan Rekayasa Alam Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59190/stc.v6i1.337

Abstract

A Classification of Quranic verses based on topics is one of the efforts to facilitate understanding and searching for information in the holy book, especially for non-Arabic readers. This study aims to test and compare the performance of three text classification methods, namely K-nearest neighbors (KNN), support vector machine (SVM), and random forest (RF), in grouping translated Quranic verses into 15 topic classes, such as Islamic arkanul, faith, the Quran, science and its branches, charity, da'wah, jihad, human and social relations, and others. The dataset used is the English translation of the Quran with full preprocessing and an 80:20 data split for training and testing. The evaluation was carried out using accuracy, precision, recall, and F1-score metrics. The results show that RF achieved the best performance with an average F1-score of 58.48% and testing accuracy of 90.81%. KNN followed with an F1-score of 54.07% and the highest testing accuracy of 92.05%, while SVM produced the lowest F1-score at 50.76% and accuracy of 88.20%. The RF demonstrates a more balanced ability in recognizing all classes, KNN excels in overall accuracy, and SVM performs less optimally in this classification task. This research is expected to serve as a foundation for developing a more intelligent and contextual topic-based verse classification system.
Chitosan from Gonggong Snail Shells to Reduce Iron (Fe) Levels in Dug Well Water in Andana Residence Housing, Batu IX Village, Riau Islands Horiza, Hevi; Iskandar, Iwan; Yuhesti, Mutia
Aloha International Journal of Health Advancement (AIJHA) Vol 6, No 8 (2023): August
Publisher : Alliance oh Health Activists (AloHA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33846/aijha60802

Abstract

Dug well water often contains organic and inorganic components, including various dangerous metals that are commonly found in it, such as iron (Fe). Therefore, this research aimed to determine the combination of aeration and filtration methods using chitosan from gonggong shells to reduce Fe levels in dug well water in the Andana Housing Complex, Tanjungpinang City. This research implemented a pre-experimental design. Water samples were obtained from 10 dug wells in the Andana Housing Complex, Tanjungpinang City. The samples were treated with a combination of aeration and filtration methods using chitosan from gonggong shells. Before and after treatment, physical conditions of the water were observed and laboratory tests were carried out to measure Fe levels. After treatment, it was discovered that the smell, color and taste disappeared. Meanwhile, there was also a decrease in Fe levels of 80.95% for gonggong snail shell chitosan with a thickness of 1 cm, 84.93% for a thickness of 3 cm, and 98.33% for a thickness of 5 cm. It was concluded that the combination of aeration and filtration methods using chitosan from gonggong shells was effective in improving the physical conditions of water and reducing Fe levels in water. Suggestions for further research include: improving this research by adding other media to reduce Fe levels or combining it with filtering media and other methods. Suggestions for the community to process chitosan from gonggong snail shells in water treatment containing Fe and offer the government to socialize the use of chitosan filtration from gonggong snail shells. Keywords: dug well; Fe; gonggong snail shell; chitosan
Testing the Effectiveness of Herbal Mouthwash Made from Betel Leaves and Kalamansi Orange Iskandar, Iwan; Horiza, Hevi; Yuhesti, Mutia
Aloha International Journal of Health Advancement (AIJHA) Vol 6, No 8 (2023): August
Publisher : Alliance oh Health Activists (AloHA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33846/aijha60803

Abstract

The oral cavity is the most complex and easily accessible site for microbial colonization in the human body. Teeth, gingiva, tongue, and buccal mucosa have different surfaces for microbial colonization. To reduce microbes in the oral cavity, medicinal plants can be used. Medicinal plants that can be utilized include Betel Leaves and Kalamansi orange. Betel leaves are well-known in Indonesian society not only for their traditional consumption but also for their widespread availability throughout Indonesia. The study aimed to develop a new mouthwash candidate using organic materials without the addition of alcohol compounds. This research was a laboratory experimental study, involving the preparation of betel leaf extract, the production of Betel Kalamansi Mouthwash (Sirkala), and testing the Betel Kalamansi Mouthwash (Sirkala) against two types of bacteria, Streptococcus mutans and Bacillus cereus, compared to the patented Listerine mouthwash. The stages include organoleptic testing and data processing. The expected outcome of this research is the development of an alcohol-free mouthwash made from organic materials. Microbiological effectiveness test results show that the herbal mouthwash formula Sirkala can effectively eliminate the target bacteria Streptococcus mutans, with a killing ability of up to 99.999% at contact times of 10, 15, and 20 seconds. This is comparable to the results of testing Listerine mouthwash widely available in the market. However, based on testing against Bacillus cereus, the herbal mouthwash formulation Sirkala is unable to eliminate the target bacteria like Listerine mouthwash. Keywords: efficacy testing; herbal mouthwash; betel leaf; calamansi orange
KLASIFIKASI TINGKAT KECANDUAN INTERNET TERHADAP REMAJA PEKANBARU MELALUI PENDEKATAN ALGORITMA NAÏVE BAYES Fikri, Mhd Ikhsanul; Budianita , Elvia; Iskandar, Iwan; Cynthia , Eka Pandu
ZONAsi: Jurnal Sistem Informasi Vol. 6 No. 2 (2024): Publikasi Artikel ZONAsi: Periode Mei 2024
Publisher : Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/zn.v6i2.20191

Abstract

Penggunaan internet terus meningkat di kalangan remaja. Namun, kemampuan remaja dalam memilah aktivitas internet yang bermanfaat belum sepenuhnya terwujud. Menurut survei APJII 2022, penggunaan internet pada usia 13-18 tahun meningkat hingga 99,16%. Hal ini menunjukkan peningkatan signifikan terhadap kecanduan internet. Sehingga dilakukan penelitian untuk mengevaluasi akurasi klasifikasi kecanduan internet terhadap remaja Pekanbaru menggunakan data mining dengan algoritma Naïve Bayes. Data yang digunakan sebanyak 510 data melalui kusioner. Hasil penelitian dalam klasifikasi menerapkan pengujian 10-Fold Cross Validation dengan model data latih 459 data dan diuji pada 51 data untuk pengujian. Didapatkan bahwa nilai akurasi tertinggi yaitu pada fold ke-3 dengan nilai 98% memiliki nilai precision, recall, dan f1-score adalah 98%, 99%, dan 98%. Untuk nilai akurasi terendah yaitu pada fold ke-1 dengan nilai 86% memiliki nilai precision, recall, dan f1-score adalah 86%, 87%, dan 86%. Untuk performa rata-rata yang diperoleh melalui hasil 10-fold Cross Validation menunjukkan bahwa nilai accuracy, precision, recall, dan f1-score adalah 93%, 87,3%, 89,9%, dan 88,1%. Berdasarkan hasil rata-rata akurasi yang diperoleh sebesar 93% menunjukkan metode Naïve Bayes dapat mengklasifikasikan tingkat kecanduan internet yang terdiri atas 4 kelas yaitu normal, mild, moderate, severe.
Analisis dan Desain Data Center RSUD Arifin Achmad Pekanbaru Menggunakan Standarisasi TIA 942 Syaputra, Alviandy; Iskandar, Iwan; Darmizal, Teddie; Novriyanto, Novriyanto; Safaat, Nazruddin
Jurnal Informatika Universitas Pamulang Vol 8 No 4 (2023): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v8i4.36564

Abstract

Arifin Achmad Regional General Hospital (RSUD) has a large amount of patient data so it requires a data center to store and manage all the data. In this study, an analysis of the data center at RSUD Arifin Achmad was carried out using the TIA-942 standard. Based on the results of observations that have been made, it is obtained that the current condition of the data center has several shortcomings, including the electrical system that does not yet have a private generator as a redudant, the security system that is still minimal, and the room conditions that are still limited. Based on these problems, an analysis was carried out using the PPDIOO (Prepare, Plan, Design, Implement, Operate, and Optimize) Network Life Cycle Approach method with the TIA-942 standardization approach In this research, it has been carried out up to the design stage, where at the prepare stage, search and collect related information, interview experts to gain a better understanding of the TIA-942 standard, at the planning stage (plan) a comparative analysis of the current data center with the TIA-942 standard using GAP analysis, and at the design stage (design) the design of the proposed Tier 2 data center is made. The results of this study are the current condition of the data center still in Tier 1 and provide recommendations for proposals in the form of data center designs at Tier 2 in accordance with the TIA-942 standard.
Klasifikasi Sentimen Tweet Masyarakat terhadap Kendaraan Listrik Menggunakan Support Vector Machine Ananda, Nuari; Fikry, Muhammad; Yusra, Yusra; Handayani, Lestari; Iskandar, Iwan
Jurnal Informatika Universitas Pamulang Vol 8 No 4 (2023): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v8i4.36754

Abstract

Sentiment analysis involves using classification algorithms to analyze public opinions and feelings in text. Within the automobile industry, electric vehicles (EVs) stem from the circular economy and represent a novel technology under investigation in sentiment classification studies. The Support Vector Machine (SVM) algorithm is commonly used in this research due to its superior accuracy compared to other algorithms. The goal of this study is to apply SVM variable selection techniques to enhance sentiment analysis quality. Python is the programming language used to build the sentiment classification model, which involves feature selection using TF-IDF, training with cross-validation and grid search, evaluation using a confusion matrix, and storing the dataset in a MySQL database. The research focuses on the sentiment classification of 3000 public tweets about electric vehicles on Twitter. Through various scenarios, it was observed that the accuracy of sentiment classification varied depending on factors such as randomizing data, handling negation, and using different types of features like unigrams or bigrams. The highest accuracy achieved was 84% using a scenario with random data, negation handling, and unigram features. Overall, this research highlights the impact of randomizing data and selecting appropriate features on sentiment classification accuracy for electric vehicles on Twitter.
Co-Authors Afriyanti, Liza Agnesti, Syafira Agustian, Surya Agustina, Auliyah Ahmad, Rizmah Zakiah Nur Alvinur Hidayat, Alvinur Amelia, Felina Ananda, Nuari Ananda, Sherly Arasy, Abdurrahman Astuti, Indah Dwi Aulia Ramadhani Budianita , Elvia Cynthia , Eka Pandu Darmizal, Teddie Delifah, Nur Elin Haerani Elvia Budianita Fadhilah Syafria Fauziyyah, Laila Nurul Febi Yanto Fikri, Mhd Ikhsanul Fitri Insani Gusrianti, Elsa Gusti, Siska Kurnia Hamwar, Syahbudin Handayani, Lala Syafitri Haryadi - Herdiana, Dora Horiza, Hevi Idris, M. Fadhil Idris, Muhammad Fadhil Iis Afrianty Irsyad (Scopus ID: 57204261647), Muhammad Jasril Jasril Kholilah Samosir Lawami, Reza Haya Lestari Handayani MF, M.Yusuf Muhammad Affandes Muhammad Fikry Muhammad Irsyad Mukodri, Dewi Mey Lestanti Muliani, Sarifah Nazir, Alwis Nazruddin Safaat Nazruddin Safaat H Negara, Benny Sukma Novriyanto Novriyanto Permata, Rizkiya Indah Purnama, Irgi Putri. P, Zurneli Kurnia Ragil S, Mahdhan Reski Mai Candra Ridho Hilmawan, Ridho Rinaldi Daswito Ristina Rosauli Harianja rizkaramdhaniartie, siti Sandi, Wahyu Ari Sofiyah, Wan Suwanto Sanjaya Syaputra, Alviandy Tri Prastio Nugroho Yuhesti, Mutia Yuli Novita Sari, Yuli Novita Yusra Yusra, Yusra Zulya Erda