[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119655-id":3,"doc-seo-119655-113":31,"detail-sidebar-cat-0-id-113":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},119655,962084925290,"Ophelia","https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",54,"Penelitian & Laporan","Komparasi Algoritme Machine Learning untuk Prediksi Pemeliharaan Preventif Precision Air Conditioning di Data Center","Data center merupakan fasilitas kunci layanan teknologi informasi yang harus menjamin ketersediaan agar dapat diakses secara berkelanjutan, sehingga diperlukan strategi pemeliharaan perangkat dan pemantauan operasional. Penelitian ini membandingkan beberapa algoritme klasifikasi machine learning untuk memprediksi kondisi Precision Air Conditioning (PAC) pada operasional data center. Dataset berasal dari log harian PAC di BRIN. Algoritme yang dievaluasi meliputi Decision Tree, Random Forest, Artificial Neural Network Multi-Layer Perceptron, Naïve Bayes, dan Support Vector Machine. Hasil evaluasi menunjukkan model C4.5 Decision Tree memperoleh akurasi terbaik 98,75% untuk tindakan preventif.","Komparasi Algoritme Machine Learning Untuk Prediksi Pemeliharaan Preventif Precision Air Conditioning di Data Center  \nKahfi Heryandi Suradiradja1*); Dani Ramdani1; Karno Nano1  \n1. Universitas Pamulang, Jl. Suryakencana No.1, Pamulang Bar., Kec. Pamulang, Kota Tangerang  \nSelatan, Banten 15417 Indonesia *)[Email: dosen01514@unpam.ac.id](Email: dosen01514@unpam.ac.id)  \nReceived: 29 Juni 2024 | Accepted:02 Januari 2025 | Published: 10 Januari 2025  \nABSTRACT  \nData Center is a primary facility for implementing an information technology service that applies the latest technology trends and must ensure the availability of services that can always be accessed, so supporting solutions are needed as part of the device maintenance strategy and operational monitoring process to maintain the availability of data center services. This research compares several machine learning classification algorithms to predict the condition of Precision Air Conditioning devices in data center operations. The dataset for this research is Precision Air Conditioning daily log data at the National Research and Innovation Agency. This research aims to identify several machine learning classification algorithms: Decision Tree, Random Forest, Artificial Neural Network Multi-Layer Perceptron, Naïve Bayes, and Support Vector Machine. The stages of this research method are analysis of the understanding of the maintenance status classification problem, data collection of Precision Air Conditioning log data, then continued with several machine learning algorithm modeling and evaluation to obtain an algorithm model with good accuracy results. The measurement results from the assessment of several machine learning methods in this research resulted in the C4.5 decision tree model having the best accuracy level, namely 98.75 percent. The roles generated from this model can be used to predict the condition of devices in the data center as a preventive.  \nKeywords: Algorithm, Data Center, Decision Tree, Machine Learning, Precision Air Conditioning  \nABSTRAK  \nData Center atau pusat data merupakan fasilitas dasar untukpenerapan suatu layanan teknologi informasi yang menerapkan tren teknologi terkini harus terjamin ketersediaan layanannya selalu dapat diakses, sehingga dibutuhkan solusi pendukung sebagai bagian dari strategi pemeliharaan perangkat dan proses pemantauan operasional dalam rangka mempertahankan ketersediaan layanan data center. Pada penelitian ini membandingkan beberapa algoritme klasifikasi machine learning untuk memprediksi kondisi perangkat Precision Air Conditioning pada operasional datacenter. Dataset untuk penelitian ini adalah data log harian Precision Air Conditioning di Badan Riset dan Inovasi Nasional (BRIN). Tujuan dari penelitian ini, mengidentifikasi beberapa algoritmeklasifikasi machine learning yakni Decision Tree, Random Forest, Artifcial Neural Network Multi Layer Perceptron, Naïve Bayes dan Support Vector Machine. Tahapan dari metode penelitian ini yakni analisis pemahaman terhadap masalah pengklasifikasian status pemeliharaan, pengambilan data data log Precision Air Conditioning, kemudian dilanjutkan proses beberapa pemodelanalgoritme – algoritme machine learning dan evaluasi untuk mendapatkan model algoritme dengan hasil akurasi yang baik. Hasil pengukuran pada evaluasi dari beberapa machine learning padapenelitian ini menghasilkan model decision tree C4.5 memiliki tingkat akurasi terbaik yakni 98,75 persen. Role yang dihasilkan dapat digunakan untuk memprediksi kondisi perangkat di data centersebagai tindakan preventif.  \nKata kunci: Algoritme, Data Center, Decision Tree, Machine Learning, Precision Air Conditioning  \n1. PENDAHULUAN  \nPusat data merupakan serangkaian produk dan solusi yang menyediakan sumber dayakomputasi dan penyimpanan [1] . Salah satu perangkat yang ada di data center adalah PAC (Precision Air Conditioning) yakni sistem pendingin yang menjaga suhu (18 sampai dengan 24 derajat celcius) serta kelembaban (Relative Hum","cbCaijIRHpGPI0pR","https://ap.wps.com/l/cbCaijIRHpGPI0pR","pdf",691722,4,1,11,"Indonesian","id",113,"# Pendahuluan\n## Latar Belakang dan Kebutuhan Prediksi Pemeliharaan Preventif\n## Machine Learning dan Klasifikasi\n# Metode Penelitian\n## Analisis Masalah Klasifikasi Status Pemeliharaan\n## Pengumpulan Data Log PAC\n## Pemodelan dan Evaluasi Algoritme\n# Hasil dan Pembahasan\n## Perbandingan Akurasi Beberapa Algoritme\n## Model Terbaik untuk Prediksi Preventif\n# Kesimpulan","[{\"question\":\"Penelitian ini bertujuan untuk apa?\",\"answer\":\"Membandingkan beberapa algoritme klasifikasi machine learning untuk memprediksi kondisi Precision Air Conditioning agar mendukung tindakan pemeliharaan preventif di data center.\"},{\"question\":\"Data apa yang digunakan sebagai dataset penelitian?\",\"answer\":\"Dataset berasal dari data log harian Precision Air Conditioning pada operasional data center di Badan Riset dan Inovasi Nasional (BRIN).\"},{\"question\":\"Algoritme apa yang menghasilkan akurasi terbaik?\",\"answer\":\"Model Decision Tree berbasis C4.5 memiliki akurasi terbaik dengan nilai 98,75% pada hasil evaluasi penelitian.\"}]","Komparasi Algoritme Machine Learning untuk Prediksi Pemeliharaan Preventif Precision Air Conditioning di Data Center | PDF",1785725504,17,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"comparison-of-machine-learning-algorithms-for-predicting-preventive-maintenance-of-precision-air-conditioning-in-data-centers","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/id/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/id/document/penelitian-laporan/",3,{"item":53,"name":13,"@type":44,"position":20},"https://docshare.wps.com/id/document/comparison-of-machine-learning-algorithms-for-predicting-preventive-maintenance-of-precision-air-conditioning-in-data-centers/119655/",{"url":53,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-15","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Penelitian ini bertujuan untuk apa?","Question",{"text":76,"@type":77},"Membandingkan beberapa algoritme klasifikasi machine learning untuk memprediksi kondisi Precision Air Conditioning agar mendukung tindakan pemeliharaan preventif di data center.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Data apa yang digunakan sebagai dataset penelitian?",{"text":81,"@type":77},"Dataset berasal dari data log harian Precision Air Conditioning pada operasional data center di Badan Riset dan Inovasi Nasional (BRIN).",{"name":83,"@type":74,"acceptedAnswer":84},"Algoritme apa yang menghasilkan akurasi terbaik?",{"text":85,"@type":77},"Model Decision Tree berbasis C4.5 memiliki akurasi terbaik dengan nilai 98,75% pada hasil evaluasi penelitian.","https://schema.org",{"og:url":53,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,99,103,107,111,115,117,121,125,129,133],{"id":95,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},55,"Agama & Spiritualitas",60,"religion-spirituality",{"id":100,"doc_module":4,"doc_module_name":47,"category_name":101,"show_sort_weight":97,"slug":102},48,"Cerita & Novel","story-novel",{"id":104,"doc_module":4,"doc_module_name":47,"category_name":105,"show_sort_weight":97,"slug":106},56,"Gaya Hidup","lifestyle",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":97,"slug":110},51,"Komik","comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":97,"slug":114},53,"Layanan Kesehatan","healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":97,"slug":116},"research-report",{"id":118,"doc_module":4,"doc_module_name":47,"category_name":119,"show_sort_weight":97,"slug":120},49,"Sastra","literature",{"id":122,"doc_module":4,"doc_module_name":47,"category_name":123,"show_sort_weight":97,"slug":124},52,"Teknologi","technology",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":127,"show_sort_weight":97,"slug":128},50,"Ujian","exam",{"id":130,"doc_module":4,"doc_module_name":47,"category_name":131,"show_sort_weight":97,"slug":132},57,"Umum","general",{"id":134,"doc_module":4,"doc_module_name":47,"category_name":135,"show_sort_weight":4,"slug":136},181,"Formulir","formulir"]