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All Journal Jurnal Teknologi Terpadu Jurnal EK dan BI Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Minfo Polgan (JMP) Jurnal Komtekinfo Journal of Information System and Education Development
Lubis, Siti Sahara
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences Other

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Implementasi Algoritma K-Means Guna Pengelompokkan Data Penjualan Berdasarkan Pembelian Lubis, Siti Sahara; Sarjon Defit; Sumijan
Jurnal KomtekInfo Vol. 11 No. 4 (2024): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v11i4.557

Abstract

Information technology can help solve problems faced by humans by facilitating performance. Information technology and information systems are difficult to separate in the business world. Data mining is the core of the KDD process, which involves inferring algorithms that explore data, developing models and finding previously unknown patterns. KDD is often referred to as knowledge discovery in databases. The KDD process generally consists of 5 stages, namely data selection, pre-processing/cleaning, transformation, data mining and interpretation/evaluation. K-Means is a clustering algorithm in data mining to be able to produce groups of large amounts of data with a point-based partition method with fast and efficient computing time. Clustering is the process of dividing objects from a data set into several homogeneous clusters. The main purpose of the cluster method is to group a number of data/objects into clusters (groups) so that each cluster will contain data that is as similar as possible. This study aims to provide real solutions to UD. Martua in order to know which items are selling well and which items are not selling well so that the object can know which items need to be added to the stock and which items need to be reduced. The method used in this study is the K-Means method with stages, namely data selection, pre-processing, data transformation, information extraction and evaluation of results. The data consists of 30 item data, there are 8 as members of C1 and are best-selling items and 22 as members of C2 and are not selling items. The conclusion that can be obtained from this study is that the K-Means method can group items at UD. Martua. This study shows that the implementation of the K-Means method with the support of the RapidMiner application is effective in grouping item data at UD. Martua.
Penerapan Metode Rough Set Dalam Memprediksi Penjualan Pada PT. Jaya Framex Bengkulu Lubis, Fitri Amelia Sari; Lubis, Siti Sahara; Agustin, Riris; Karmanita, Deti; Defit, Sarjon
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 9, No 1 (2024): Edisi Februari
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v9i1.758

Abstract

So far, in predicting sales at PT. Jaya Framex Bengkulu, only relies on manual calculations. There are no calculations that use a system to help predict sales at PT. Jaya Framex Bengkulu in the future. As more and more entrepreneurs emerge, it requires entrepreneurs to plan sales strategies. So that what is produced does not decrease further, and is not less competitive with other entrepreneurs, to avoid this, it is necessary to have sales predictions to predict sales so that you can plan future sales strategies. Based on the research conducted, the author can draw the conclusion that predicting the number of food products using Data Mining is very helpful in processing data that has been classified such as product supply, product type and capabilities so that it produces rules that support a decision which can later be used as support for sales prediction decisions. to be more optimal. From 13 sample data of the Data Mining sales process using the rough set method, 5 Reducts were produced which were extracted into knowledge of 11 Generate Rules, thereby producing a decision that was conveyed from the resulting rules. The results of this research can be used by developers to predict future sales. It is hoped that adding new variables can produce more varied decisions and more useful knowledge as decision support
Customer Relationship Management Dalam Meningkatkan Loyalitas Pelanggan Pada Doorsmeer Keluarga Nasution Menggunakan Metode Algoritma K-Means Lubis, Siti Sahara; Lubis, Fitri Sari; Billy Hendrik
Journal of Information System and Education Development Vol. 1 No. 2 (2023): Journal of Information System and Education Development
Publisher : Manna wa Salwa Foundation (Yayasan Manna wa Salwa)

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Abstract

Doorsmeer, the Nasution Family, which is located on Jl. Medan Padang Aek Godang Panyabungan, Mandailing Natal Regency, provides services in the form of car and motorcycle washing. Bookkeeping at the Nasution Family Doorsmeer is still done manually, allowing for loss of important transaction data, as well as a lack of business promotion in the community resulting in slow business development. The purpose of this study is generally to apply Web-based Customer Relationship Management with the K-means algorithm method for marketing/transactions at the Nasution Family Doorsmeer. This research is a combined type, using descriptive qualitative and quantitative methods. From the research results it is known that with the existence of Web-based Customer Relationship Management it can simplify and assist in managing good marketing strategies so as to increase sales revenue, and by providing the best service will encourage customer loyalty. In determining loyal customers the fields used are the customer's name, vehicle license plate, number of visits and total transactions for 2 months, then the data will be processed with the k-means clustering algorithm. The final result with 20 samples of transaction data is the final result with 2 clusters, namely, cluster 1 (C1) with 13 disloyal customers, cluster 2 (C2) with 7 loyal customers. By implementing Customer Relationship Management with K-Means, you can build customer clustering to categorize customers who use services so that service providers can identify the characteristics of their customers. With Customer Relationship Management with K-Means, promotions can be directed to customers who are entitled to get them. With the implementation of Customer Relationship Management reaches more new customers through a system that has been directly integrated with the internet network, so that new visitors or those who do not know Doorsmeer Nasution Family, both those around the business location and those who are far away, will find it easy to get to know Doorsmeer Nasution Family through internet searches so that the number customers who transact with the Nasution Family's Doorsmeer business have increased.
Implementasi Data Mining Pengelompokan Data Penjualan Berdasarkan Pembelian dengan Menggunakan Algoritma K-Means pada UD. Martua Lubis, Siti Sahara; Hendrik, Billy
Journal of Information System and Education Development Vol. 1 No. 3 (2023): Journal of Information System and Education Development
Publisher : Manna wa Salwa Foundation (Yayasan Manna wa Salwa)

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Abstract

Saat ini kita tidak bisa lepas dari pengaruh teknologi informasi. Karena mau tidak mau perkembangan ilmu pengetahuan dan penerapan teknologi mengalami peningkatan yang semakin pesat terutama pada setiap lingkungan kerja seperti praktisi bisnis.Teknologi komputer/ informasi merupakan teknologi yang paling banyak dimanfaatkan diberbagai instansi baik pemerintah maupun swasta . Perkembangan teknologi saat ini yang begitu sangat cepat dari waktu ke waktu membuat pekerjaan manusia pada umumnya dapat diselesaikan dengan cepat. Teknologi merupakan salah satu alat yang sering digunakan dalam aktivitas manusia. Peran teknologi saat ini membuat pengolahan informasi menjadi lebih mudah karena pengolahan diperlukan agar informasi yang dihasilkan dapat bermanfaat bagi penggunanya. Persaingan dalam dunia bisnis menuntut para pengembang untuk menemukan suatu pola yang dapat meningkatkan penjualan dan pemasaran barang, salah satunya adalah dengan pemanfaatan data transaksi. Ketersediaan data yang melimpah, kebutuhan akan informasi sebagai pendukung pengambilan keputusan untuk membuat solusi bisnis, dan dukungan infrastruktur di bidang teknologi informasi merupakan alasan dari lahirnya teknologi data mining. Masalah yang terjadi di UD. Martua yaitu kurang dalam peninjauan produk yang dijual, produk-produk apa saja yang dibutuhkan konsumen dan penyimpanan data-data kurang efektif. Dengan adanya data mining dimaksudkan untuk memberikan solusi nyata kepada UD. Martua agar dapat mengetahui mana barang yang laris dan mana barang yang tidak laris, kemudian dapat membandingkan penjualan dari tahun ke tahun menjadi media yang efektif untuk pengembangan penjualan pada UD.Martua. Dalam pengelompokkan data penjualan field yang digunakan adalah nama barang, jumlah beli, jumlah terjual selama 1 minggu, kemudian data akan diproses dengan algoritma k-means clustering. Hasil akhir dengan 20 sampel data pengelompokkan didapatkan hasil akhir dengan 2 cluster yaitu, cluster 1 (C1) dengan 10 barang laris , cluster 2 (C2) dengan 10 barang tidak laris. Dengan Data mining menggunakan algoritma K-Means Mempercepat untuk pengambilan keputusan untuk merestock barang yang laris agar konsumen yang ingin membeli tidak menunggu lama. Memberikan informasi dari data penjualan untuk mengetahui apa saja yang mengakibatkan keuntungan ataupun kerugian pada UD.Martua. Memberikan kemudahan bagi UD.Martua dalam menentukan produk mana yang laris dan tidak laris agar tidak terjadinya penumpukan barang yang tidak laris dan mengakibatkan kerugian pada UD.Martua.
SYSTEMATIK LITERATURE REVIEW PENGEMBANGAN POTENSI BRANDING UMKM MELALUI PROSES DIGITALISASI BISNIS Lubis, Siti Sahara; Veri, Jhon
Jurnal Ekonomi dan Bisnis (EK dan BI) Vol 7 No 1 (2024)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/ekbi.v7i1.1447

Abstract

In the current era, micro small and Medium Enterprises must use digital platforms as a marketing strategy for their products so that consumers can become more familiar with the products they produce. Service activities with the theme of Developing micro small and Medium Enterprises Branding Potential through the business digitalization process. A systematic literature review (SLR) is a secondary study to map, identify, critically express, consolidate, and collect the results of primary studies on a particular research topic. From 2,810 data, ± 50 journals were obtained which were almost similar to the research theme which would enter the screening stage using inclusion and exclusion criteria to ensure the quality and relevance of the articles to the themes raised. The results of this Community Service activity suggest that Service Partners continue use of digital marketing strategies and receive regular guidance. This aims to ensure the effectiveness and optimality of this strategy in supporting micro small and Medium Enterprises marketing and increasing the sales turnover of MSME businesses in the future.
Implementasi Metode Case-Based Reasoning (CBR) dalam Sistem Pakar untuk Mendapatkan Diagnosis Anxiety Disorders Gunung, Tar Muhammad Raja; Lubis, Siti Sahara; Siregar, Manutur; Simanjuntak, Peter Jaya Negara; Jinan, Abwabul
Jurnal Teknologi Terpadu Vol 10 No 2 (2024): Desember, 2024
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v10i2.1480

Abstract

This research aims to develop an expert system based on the case-based reasoning method for diagnosing anxiety disorders. Anxiety Disorder is a mental health disorder that is often experienced by the public but is often not detected correctly. The case-based reasoning method was chosen because of its ability to utilise previous cases to solve new problems that have similarities. Case-based reasoning uses four main stages: retrieval, reuse, revise, and retain. The case-based reasoning method is implemented using case data obtained from psychology clinics and interviews with mental health experts. Testing the case-based reasoning method shows a high level of accuracy in diagnosing various types of Anxiety Disorders, such as Generalised Anxiety Disorder, Panic Disorder, and Specific Phobias. The results of this study show that the case-based reasoning method can be an effective tool in helping mental health professionals diagnose Anxiety Disorders more quickly and accurately. After searching using the symptoms obtained, the percentage of each type of disease is the percentage of Generalised Anxiety Disorder 35.7%, the percentage of Panic Disorder 30.7%, and the percentage of Specific Phobias 65%.
Analisis Pengendalian Bahan Baku Utama Produksi Bubuk Kopi Menggunakan Metode Economic Order Quantity (EOQ) Lubis, Fitri Amelia Sari; Lubis, Siti Sahara; Selvanda, Alifia Restu
Jurnal Minfo Polgan Vol. 14 No. 1 (2025): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v14i1.14993

Abstract

Persediaan bahan baku merupakan salah satu aspek krusial dalam kegiatan produksi suatu usaha. Manajemen persediaan yang tidak efisien dapat menyebabkan ketidakseimbangan antara permintaan dan ketersediaan bahan baku, yang pada akhirnya berpengaruh terhadap kelancaran proses produksi serta efisiensi biaya operasional. Kesalahan dalam menentukan jumlah pemesanan dan waktu pemesanan dapat mengakibatkan stok berlebih yang meningkatkan biaya penyimpanan atau stok yang terlalu sedikit sehingga dapat menghambat produksi dan menyebabkan kehilangan peluang penjualan. Oleh karena itu, diperlukan sistem pengelolaan persediaan yang optimal untuk menekan biaya penyimpanan, menghindari kekurangan stok, dan meningkatkan efisiensi proses produksi. Penelitian ini bertujuan untuk menganalisis pengendalian bahan baku utama produksi bubuk kopi dengan menerapkan metode Economic Order Quantity (EOQ) pada Usaha Bubuk Kopi Rumah Gadang, sebuah industri pengolahan bubuk kopi yang berlokasi di Batusangkar, Sumatera Barat. Metode EOQ digunakan untuk menentukan jumlah pemesanan bahan baku yang optimal guna meminimalkan total biaya persediaan, yang terdiri dari biaya pemesanan dan biaya penyimpanan. Selain itu, penelitian ini juga mengidentifikasi pentingnya safety stock dalam menjaga ketersediaan bahan baku guna mengantisipasi fluktuasi permintaan pasar dan keterlambatan pengiriman dari pemasok.
Co-Authors Agustin, Riris Billy Hendrik Deti Karmanita Hendrik, Billy Jinan, Abwabul Lubis, Fitri Amelia Sari Lubis, Fitri Sari Manutur Siregar Sarjon Defit Sarjon Defit Selvanda, Alifia Restu Simanjuntak, Peter Jaya Negara Sumijan Tar Muhammad Raja Gunung Veri, Jhon