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INDONESIA
Jurnal Ilmiah Informatika Komputer
Published by Universitas Gunadarma
ISSN : 08538638     EISSN : 20898045     DOI : http://dx.doi.org/10.35760/ik
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
This journal is published periodically three times a year, April, August, and December. It publishes a broad range of research articles on Information Technology and Communication, whether in Indonesian Language or English.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 8 Documents
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Search results for , issue "Vol 29, No 1 (2024)" : 8 Documents clear
PREDIKSI TINGKAT KUALITAS UDARA DENGAN PENDEKATAN ALGORITMA K-NEAREST NEIGHBOR Nesya Syahira; Dede Brahma Arianto
Jurnal Ilmiah Informatika Komputer Vol 29, No 1 (2024)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/ik.2024.v29i1.10069

Abstract

Air is the source of life for living things. However, in recent years the decline in air quality has become a serious problem that urgently needs to be addressed. The Air Pollution Standard Index (ISPU) is an indicator that can be used to determine the condition of ambient air quality in a particular location. Yogyakarta is one of the major cities in Indonesia with serious air pollution problems in recent years. This research aims to predict air quality in Yogyakarta City based on ISPU data using data mining techniques and classification methods. The algorithm used in prediction is K-Nearest Neighbor (K-NN), which classifies new objects based on their nearest neighbors. Evaluation of the algorithm model is done by measuring accuracy, precision, recall, and f-measure for the value of K = 5. The test results show that the value of K = 5 provides good performance with an accuracy of 99% which is for the "Good" category producing 100% precision, 99% recall, and 100% f-measure, while the "Moderate" category produces 98% precision, 100% recall, and 99% f-measure.
PENERAPAN METODE FUZZY TSUKAMOTO UNTUK PENENTUAN HARGA RUMAH DI KOTA BANDUNG Pawit Widiyantoro; Rosita Dian Febriyanti; Cheppi Garda Muhamad
Jurnal Ilmiah Informatika Komputer Vol 29, No 1 (2024)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/ik.2024.v29i1.10598

Abstract

Rumah merupakan salah satu kebutuhan pokok manusia yang harganya selalu naik tiap tahunnya. Prediksi harga rumah dapat membantu seseorang untuk mengetahui harga rumah dengan faktor-faktor tertentu. Penelitian ini bertujuan untuk memprediksi harga rumah di kota Bandung menggunakan logika fuzzy. Digunakan metode Fuzzy Tsukamoto dalam penelitian ini dengan menggunakan 3 variabel masukan yaitu jumlah kamar, luas bangunan, dan luas lahan dengan keluaran yaitu variabel harga rumah. Variabel masukan yang digunakan disusun ke dalam 18 rule fuzzy dan diuji menggunakan 5 data. Hasil perhitungan kemudian dievaluasi menggunakan Mean Average Error (MAE). Hasil perhitungan menunjukan nilai MAE sebesar 34.691.272,8 yang jika dipersentasekan terhadap harga rata-rata akan menjadi 7,5%. Hasil ini menunjukan bahwa metode Fuzzy Tsukamoto yang digunakan dalam penelitian ini cukup baik untuk memprediksi harga rumah di Kota Bandung.
SISTEM KEAMANAN LEMARI PENYIMPANAN BARANG MENGGUNAKAN PASSWORD SEBAGAI KENDALI AKSES DAN SENSOR ULTRASONIK SEBAGAI PENDETEKSI BARANG Muhammad Akbar; Robby Candra; Yuli Karyanti; Yulia Chalri; Hasma Rasjid
Jurnal Ilmiah Informatika Komputer Vol 29, No 1 (2024)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/ik.2024.v29i1.9861

Abstract

A sense of security in storing items and ease of finding storage cabinets in public places such as shopping centers is absolutely necessary, but this may still not be fulfilled because storage cabinets still use conventional locks or still use cards to store items, plus it requires a time-consuming process to look for an empty storage cabinets. Therefore, the creation of a storage cabinets model in this research aims to implement a security system for a storage cabinets with access control using a password and to know whether there are items in the storage cabinets or not so that users can find out which storage cabinets are available/empty. This storage cabinets security device uses a solenoid as a lock, a servo motor to actuate the cabinets door, an ultrasonic sensor to detect items in the cabinets and control it via keypad and LCD to display the condition of the cabinets use. This tool uses an Arduino Mega microcontroller as the main controller and password storage medium, thereby producing output automatically where the solenoid will open/close and the servo motor moves to open/close the cabinets door. The tool can function to detect items in cabinets and based on item detection using an ultrasonic sensor the user can find out which cabinets are empty and the cabinets door will open based on a password determined by each user to ensure the security of the items stored.
IMPLEMENTASI PENGGUNAAN ALGORITMA A* PADA PENENTUAN JARAK TERPENDEK DARI CILACAP KE YOGYAKARTA Ahmad Satria Rizqi Maula; Tundo Tundo; Sopan Adrianto; Kastum Kastum; Nandang Sutisna
Jurnal Ilmiah Informatika Komputer Vol 29, No 1 (2024)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/ik.2024.v29i1.10661

Abstract

Penelitian ini bertujuan untuk meningkatkan efisiensi penentuan rute terpendek dari Cilacap ke Yogyakarta dengan menerapkan Algoritma A*. Metode penelitian ini melibatkan tahap pengumpulan data perjalanan untuk memahami kondisi jalan dan parameter lain yang memengaruhi perjalanan. Data tersebut kemudian digunakan untuk membentuk representasi graf, menciptakan suatu model yang merefleksikan koneksi antar lokasi dan memfasilitasi implementasi Algoritma A*. Penggunaan Algoritma A* memungkinkan penemuan jalur terpendek dengan meminimalkan biaya perjalanan. Hasil analisis menunjukkan bahwa implementasi ini berhasil meningkatkan efisiensi penentuan rute dengan perbedaan waktu tempuh yang signifikan dibandingkan dengan metode tradisional. Dalam konteks perancangan keputusan, penelitian ini mengintegrasikan kemampuan pengambilan keputusan otomatis berdasarkan analisis data, memungkinkan sistem untuk merespons perubahan kondisi jalan secara dinamis. Dengan demikian, penelitian ini tidak hanya meningkatkan akurasi penentuan rute tetapi juga berpotensi memberikan kontribusi pada pengembangan sistem navigasi yang adaptif dan efisien. 
IMPLEMENTASI DATA MINING ALGORITMA DECISION TREE UNTUK KLASIFIKASI STATUS GIZI BALITA DI KECAMATAN CILEDUG Siti Bulkisah Bulkisah; Rini Astuti; Agus Bahtiar
Jurnal Ilmiah Informatika Komputer Vol 29, No 1 (2024)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/ik.2024.v29i1.10346

Abstract

The nutritional intake plays a crucial role in supporting the physical development of toddlers; however, not all toddlers in Ciledug District receive adequate nutrition. The number of toddlers experiencing nutritional status disorders or nutritional problems fluctuates annually, influenced by the fluctuation in the total number of toddlers. Currently, 2.9% of toddlers in Ciledug District are experiencing nutritional status disorders. This study aims to implement a classification process to determine the nutritional status of toddlers in Ciledug District using the decision tree algorithm. The achieved accuracy of the results is 99.18%, with detailed predictive outcomes as follows: 2298 instances correctly predicted as normal nutrition, 23 instances correctly predicted as abnormal nutrition, 2290 instances correctly predicted as abnormal nutrition, and 15 instances correctly predicted as normal nutrition. The classification results based on age indicate that infants aged 2 weeks have normal nutrition, toddlers aged 1 to 11 months exhibit both normal and abnormal nutrition, toddlers aged 12 months have normal nutrition, toddlers aged 13 to 58 months show both normal and abnormal nutrition, and toddlers aged 59 to 61 months have normal nutrition.
ANALISIS MINAT BELI PRODUK FASHION MENGGUNAKAN ALGORITMA FP-GROWTH (FREQUENT PATTEN GROWTH) Tineka Handayani; Muhamad Aditya Sunaryo; Ade Rizki Rinaldi; Cep Lukman Rohmat
Jurnal Ilmiah Informatika Komputer Vol 29, No 1 (2024)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/ik.2024.v29i1.10808

Abstract

Toko Sahabat Collection adalah toko pakaian yang menjual berbagai produk fashion dan memiliki data penjualan yang sangat besar. Penjualan produk fashion di toko ini sangat diminati oleh pelanggan. Penelitian mencakup informasi penjualan produk dari tahun 2021 hingga tahun 2022, termasuk tanggal transaksi, code produk, barcode, dan item produk. Langkah awal dalam penelitian ini melibatkan pemilihan data dengan memilih atribut yang relevan dan menghapus data yang tidak lengkap. Selanjutnya, data yang sudah divalidasi diubah menjadi format tabular. Proses selanjutnya melibatkan penerapan teknik data mining, seperti algoritma asosiasi rule, untuk meningkatkan target penjualan. Penelitian ini bertujuan untuk mengetahui pola minat beli produk yang diminati oleh pelanggan di toko sahabat collection yang dapat dipengaruhi oleh beberapa faktor seperti harga kualitas produk dan kebutuhan customer. Minat beli konsumen dianggap sebagai faktor kunci yang sangat penting dalam ruang lingkup bisnis, penelitian ini menerapkan metode data mining asosiasi, dengan fokus khusus pada algoritma FP-Growth, untuk menganalisis pola pembelian produk secara bersamaan. Hasil dari penelitian ini menunjukkan 32 aturan dengan minimum support 0,1 dan confidence 0,8. Salah satu aturan terbaik yang dihasilkan adalah: jika pelanggan membeli Hijab Ar Rafi, Hijab Bergo Pita, dan Kaos Kaki Hitam Smp, maka kemungkinan besar mereka juga akan membeli Sepatu Nb K, support 0,105 (10%) dan Confidence 0,949 (94%).
PREDIKSI KEPUASAN PELANGGAN HOTEL: STUDI PERBANDINGAN ALGORITMA DECISION TREE DAN KNEAREST NEIGHBOR Dwi Ramti Asih; Rianto Rianto
Jurnal Ilmiah Informatika Komputer Vol 29, No 1 (2024)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/ik.2024.v29i1.8324

Abstract

Customer satisfaction has become an important aspect for every business in today's competitive market. Understanding customer needs, wants, and expectations is critical for a business to provide outstanding customer service and retain customers. Therefore, this research represents a comparative study between two machine learning algorithms, Decision Tree and K-Nearest Neighbor, to predict hotel customer satisfaction. This study aims to identify which algorithm is more effective in predicting customer satisfaction by evaluating their performance using various metrics. The methodology used includes data preprocessing, feature selection, and machine learning model creation. The results show that the Decision Tree algorithm is superior to the K-Nearest Neighbor in terms of accuracy and precision. The findings from this study provide insights for businesses in the hospitality industry on how to predict customer satisfaction and improve their services.
Front Matter Jurnal Ilmiah Informatika Komputer Vol. 29 No. 1, April 2024 Editorial Jurnal Ilmiah Informatika Komputer
Jurnal Ilmiah Informatika Komputer Vol 29, No 1 (2024)
Publisher : Universitas Gunadarma

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

Abstract

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