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Darius Andana Haris
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JIKSI (Jurnal Ilmu Komputer dan Sistem Informasi)
Published by Universitas Tarumanagara
ISSN : 23028769     EISSN : 23032529     DOI : -
Core Subject : Science, Education,
Computer Science & IT Mathematics Other
Jurnal Ilmu Komputer dan Sistem Informasi (JIKSI) diterbitkan oleh Fakultas Teknologi Informasi Universitas Tarumanagara (FTI Untar) Jakarta sebagai media publikasi karya ilmiah mahasiswa program studi Teknik Informatika dan Sistem Informasi FTI Untar. Karya-karya ilmiah yang dihasilkan berupa hasil penelitian kualitatif dan kuantitatif, perancangan sistem informasi, analisis dan perancangan progam aplikasi. Jurnal ini terbit dua kali dalam setahun yaitu pada bulan Januari dan Agustus.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 937 Documents
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PENGENALAN OBJEK MENGGUNAKAN METODE SINGLE SHOT MULTIBOX DETECTOR PADA BAHAN SEMBAKO Henry Tanujaya; Lina
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 11 No. 1 (2023): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v11i1.24067

Abstract

Bahan sembako adalah singkatan dari sembilan bahan pokok yang artinya diperlukan oleh masyarakat secara umum sebagai kebutuhan sehari – hari. Bahan sembako sangat beragam jenisnya seperti minyak, beras, susu, dan masih banyak lagi. Bahan sembako biasanya dapat ditemui di supermarket, toko eceran, maupun warung kecil. Supermarket, toko eceran, dan warung kecil menjadi penyedia banyak barang dan salah satunya bahan sembako untuk dibeli oleh masyarakat umum. Penyedia yang sangat memiliki banyak kebutuhan jenis bahan sembako biasanya terdapat di supermarket. Untuk supermarket dan toko eceran biasanya memiliki data stok barang masing – masing agar mengetahui jumlah barang mereka di rak penjualan. Pengecekan stok barang juga dilakukan untuk mengetahui tanggal kedaluwarsa, kualitas barang, dan lainnya. Metode Single Shot Multibox Detector sudah banyak digunakan untuk pengenalan objek atau pengenalan objek seperti aplikasi pengenalan benda, makhluk hidup, makanan, bahkan pengenalan wajah sekalipun. Kelebihan metode ini adalah kecepatan dan keamanan yang tidak kalah bagus dengan metode lain seperti YOLO dan Fast R-CNN. Jika dibandingkan, metode SSD dapat jauh lebih tinggi keakuratannya dan kecepatan proses pengenalan objek.
Pembuatan Aplikasi Peramalan Penjualan Susu Sapi Perah Menggunakan Extreme Learning Machine Lubby Gennady; Dyah Erny Herwindiati; Janson Hendryli
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 11 No. 1 (2023): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v11i1.24069

Abstract

UMKM XYZ merupakan salah satu usaha yang bergerak dalam bidang susu sapi perah. Usaha tersebut menjual susu sapi segar dengan varian rasa. Dalam penjualan tersebut menyalurkan susu sapi segar sesuai dengan permintaan konsumen. Setiap periode, usaha sering mengalami perubahan permintaan susu sapi segar. Akibatnya, apabila permintaan konsumen tidak sesuai dengan ketersediaan dapat mengalami kerugian karena susu sapi tidak memiliki waktu simpan yang cukup lama. Oleh sebab itu, dilakukan rancangan aplikasi peramalan penjualan berbasis Android dengan metode Extreme Learning Machine (ELM) untuk menangani kasus tersebut. Tujuan dari aplikasi tersebut adalah melakukan peramalan permintaan susu agar sesuai dengan jumlah permintaan yang dibutuhkan. yang telah dilakukan dengan pengujian Black Box Testing dan hasil evaluasi kesalahan pada metode Extreme Learning Machine dengan nilai MAPE. Hasil pengujian pada Black Box Testing menunjukkan bahwa aplikasi telah layak untuk melakukan pencatatan transaksi. Hasil tersebut didapatkan nilai terbaik menggunakan aktivasi sigmoid biner dengan 4 fitur, jumlah hidden neuron sebanyak 10 serta pembagian data training dan testing yaitu 90%:10%. Hasil nilai kesalahan pada model original didapatkan nilai kesalahan MAPE 6.6558%, model coklat dengan nilai kesalahan 5.624%, model stroberi dengan nilai kesalahan 6.2874%.
IMPLEMENTASI METODE AGGLOMERATIVE HIERARCHICAL CLUSTERING UNTUK SISTEM REKOMENDASI FILM Vanesa Nellie; Viny Christanti Mawardi; Novario Jaya Perdana
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 11 No. 1 (2023): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v11i1.24070

Abstract

People can now watch movies on their cellphones or other devices using applications, in addition to watching them on television or in theaters. The user's entered keywords are used as the basis for a system that suggests movies from among the many that have appeared over time. Later, similarity between these keywords and text data, such as movie titles and descriptions, will be assessed. This recommendation system will include preprocessing, and the TF-IDF method will be used to determine the weight value. After the weight values have been determined, the grouping calculations will be performed using agglomerative hierarchical clustering. Previously, the Manhattan Distance method will be used to calculate the distance. After that, the distance that is closest can be determined. The data will be clustered according to the shortest distance once the distance calculation is complete. Following that, the system will display the grouping as a dendrogram. The data used was updated as of the date of scraping, which is November 25, 2022, and contains a total of 2467 data. The Agglomerative Hierarchical Clustering method yielded the best silhouette coefficient value, 0.5025559374455285, forming 20 clusters.
PENERAPAN DATA MINING MENGGUNAKAN HIERARCHICAL K-MEANS BERDASARKAN MODEL RFM Andreas Lie; Teny Handhayani
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 11 No. 1 (2023): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v11i1.24071

Abstract

E-commerce has become an inseparable part of the global retail work environment, where this has an impact by presenting new business competition so that companies are required to determine strategies to be able to gain profits. The purpose of this study is to produce customer segmentation using a combination of Hierachical K-Means Clustering algorithms on online retail transaction data that is transformed into Recency, Frequency, and Monetary (RFM) forms and obtain grouping results that have a high degree of similarity by evaluating clusters that are formed using Silhouette analysis. The results of the study stated that the validation test using the Silhouette Coefficient of the combination of the Hierachical K-Means Clustering algorithm was superior to the K-Means algorithm with the optimal coefficient value of the combination of the K-Means algorithm and the Ward method of Hierachical Clustering, namely 0.54027 with the number of k = 4 while the Hierachical Clustering method was K-Means is only 0.44060 with a total of k = 3. Clustering produces two groups of customers, namely Uncertain and Best Customers according to the customer value matrix.
CLUSTERING BERITA SEPAK BOLA DENGAN METODE K-MEANS Riyanto, Radika Yudha; Mawardi, Viny Christanti; Perdana, Novario Jaya
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 11 No. 1 (2023): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v11i1.24072

Abstract

Until now, many Indonesian people like soccer, both domestically and abroad. With so many football enthusiasts, people are becoming more active in finding news related to football. As time goes by, the amount of news circulating on the internet will also be more and more widespread. The large number of news makes the news need to be clustered or clustered to make it easier to access existing news. The website created is intended to group soccer news from several websites, namely: vivagoal.com, goal.com and bolasport.com. The method used on this sbobet is handicap to group news into clusters, then the method used to evaluate the quality of the clusters formed is the Silhouette coefficient method. The Silhouette coefficient value is 0.54, which means that the quality of the cluster formed is moderate.
ANALISA TOPIK TERHADAP KOMENTAR MENGENAI METAVERSE MENGGUNAKAN METODE CLUSTERING K-MEANS Andre Ertanto; Viny Christanti Mawardi; Novario Jaya Perdana
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 11 No. 1 (2023): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v11i1.24073

Abstract

The development of the internet doesn't stop there, but continues to develop and evolve, even in a game, humans can interact with each other, make transactions with each other and maybe it becomes an opportunity to earn income, one that combines all of these things is known as the Metaverse. Metaverse is a layer that connects two worlds, namely: the real world and the virtual world. Metaverse offers a 3-dimensional experience that can be shared between users and interact within this technology where every activity of its users can be carried out with the help of Augmented and Virtual Reality technology services. In the metaverse, people want to see what topics are contained in the discussion. So a website was created to determine the topic of metaverse comments from social media. The method used on this website is Clustering K-means. Use this method to divide comments into groups that have something in common. The group of comments will be determined by the topic of the highest frequency of words. Evaluation uses the Elbow Method to determine the optimal k value in Clustering K-means.
WEBSITE REKOMENDASI DAN KLASIFIKASI LAGU MENGGUNAKAN METODE WEIGHTED K-NEAREST NEIGHBOR Caroline Wili Harto; Viny Christanti Mawardi; Novario Jaya Perdana
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 11 No. 1 (2023): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v11i1.24074

Abstract

As the years went by, music has become one of the most evolving aspects of human history. There is a load of musical development around the globe, especially in music genres. Due to these differences and developments, a design was created to be able to make song recommendations according to the genre types and classifications of music or song. The data that is processed as training data is in the form of song metadata with various music features sourced from Spotify. Song recommendations are performed using the Euclidean Distance calculation between musical features or songs, while song classification is carried out using the Weighted K-Nearest Neighbor (WKNN) method calculation through audio wave type file analysis which then takes the musical features and calculates them based on the existing song or music data. The end result of this process is the genre class label. There is also a classification evaluation calculation using a confusion matrix. With the design of this system, it is hoped that the user will be able to search for song recommendations that have similarities to the song chosen by the user and classify genres according to the user's input song.
Rancangan Sistem Prediksi Harga Saham dengan Menggunakan Metode LSTM dan ARMA klasik Caesar Calendo Sumarga; Dyah Erny Herwindiati; Janson Hendryli
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 11 No. 1 (2023): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v11i1.24075

Abstract

Stocks are one of the types of assets that are currently popular with the wider community, just like gold and all other types of assets, the value of stocks tends to move up and down over time, therefore stock investors invest in stocks to achieve the desired profit (capital gain), Due to the movement of stocks that go up and down over time, it is difficult for investors to determine when to buy or sell stocks, therefore this study was conducted to compare the multivariate Long-Short Term Memory (LSTM) method, and the classic ARMA, then see which is suitable in forecasting stock prices, the comparison is seen from the results of the error evaluation metrics of the two methods.
Market Basket Analysis dengan Perbandingan Metode Apriori dan FP-Growth Pada Data Transaksi XYZ Rizki Nofrian Wahyudi; Dyah Erny Herwindiati; Janson Hendryli
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 11 No. 1 (2023): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v11i1.24077

Abstract

Technology is currently advancing quickly, allowing all organizations to grow their networks with its aid and create sales methods that now rely on technology to aid in making the proper judgments. When saved transaction data is accessible, every business will be able to implement its marketing strategy to maximize client transactions. use it to your advantage. Analysis of the market basket using the FP-Growth and a priori algorithm in transactions that aid in strategic planning and business product structuring. The FP-Growth algorithm and the Apriori algorithm work well together. One can evaluate the effectiveness of the employment of the a priori algorithm and the FP-Growth algorithm by applying both of them
KLASIFIKASI RAS ANJING MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK DENGAN ARSITEKTUR VGG-16 Sandy Danish Arkansa; Chairisni Lubis
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 11 No. 1 (2023): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v11i1.24078

Abstract

Dogs are lovable animals that bring companionship and fun to any household, but things to consider before getting a dog are their daily needs including food, shelter, and animal care, as well as affection and physical and mental stimulation. This research was conducted to introduce dog breeds and how to care for each breed. This system is built using Mask R-CNN and Convolutional Neural Network (CNN), a deep learning architecture. Mask R-CNN model is used for detecting and cropping dog in images, trained using Microsoft Common Object of Context (MS COCO) dataset. CNN is used for classification of dog breeds in images and is trained using 17.513 images of 17 different breeds. Result for Mask R-CNN show the detection accuracy for dogs has 74% using test images, and CNN show the identification accuracy using test images has 82% accuracy, and for CNN using cropped images has 87% accuracy.

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