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APLIKASI PENDAFTARAN SISWA BARU DENGAN SISTEM SELEKSI MENGGUNAKAN METODE SIMPLE ADDITIVE WEIGHTING (SAW) PADA SMK MIFTAHUL HUDA CIWARINGIN Lena Magdalena; Abdul Rachman
Jurnal Digit Vol 7, No 1 (2017)
Publisher : Universitas Catur Insan Cendekia (CIC) Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51920/jd.v7i1.14

Abstract

AbstrakPendaftaran siswa baru merupakan suatu kegiatan yang wajib dilakukan pihak penyelenggara sekolah yang bertujuan untuk menampung, menyaring, serta menyeleksi para calon peserta didik sebelum dinyatakan sebagai peserta didik tetap. SMK Miftahul Huda Ciwaringin adalah salah satu sekolah kejuruan yang terus berkembang  dengan bertambahnya jumlah siswa baru yang mendaftar setiap tahunnya. Akan tetapi proses pendaftaran siswa baru di SMK Miftahul Huda Ciwaringin masih menggunakan metode manual yang mengakibatkan banyaknya waktu yang terbuang dalam melakukan proses pendaftaran sehingga menyebabkan berkurangnya efisiensi waktu dari calon siswa. Sesuai dengan peraturan PPDB 2015 yang sudah ditentukan oleh pihak SMK Miftahul Huda Ciwaringin untuk menyeleksi calon siswa, maka diperlukan kriteria-kriteria untuk penentuan dalam menetapkan seorang siswa baru, maka dibutuhkan sebuah sistem dengan metode Simple Additive Weighting (SAW). Metode SAW ini mengharuskan pembuat keputusan menentukan bobot bagi setiap atribut. Skor total untuk alternatif diperoleh dengan menjumlahkan seluruh hasil perkalian antara rating (yang dapat dibandingkan lintas atribut) dan bobot tiap atribut. Sistem ini akan menampilkan prioritas-prioritas tertinggi hingga terendah dari calon-calon siswa tersebut, sehingga akan memudahkan dan membantu pihak sekolah dalam mengambil keputusan. Dengan menggunakan metode SAW dalam sistem seleksi siswa baru di SMK Miftahul Huda Ciwaringin bertujuan untuk memudahkan panitia  dalam menentukan perankingan calon siswa untuk menyeleksi siswa yang memiliki skor nilai yang sama, mempermudah proses dalam sistem seleksi penerimaan calon siswa baru di SMK Miftahul Huda Ciwaringin. Kata Kunci : Pendaftaran, Miftahul, SAW, Seleksi, SMK.
PENERAPAN SISTEM KEPUTUSAN DENGAN METODE ROC DAN MOORA UNTUK LOWONGAN PEKERJAAN BAGI ALUMNI UNIVERSITAS CATUR INSAN CENDEKIA Lia Dahlia; Lena Magdalena; Kusnadi Kusnadi
JTIK (Jurnal Teknik Informatika Kaputama) Vol 7, No 1 (2023): Volume 7, Nomor 1 Januari 2023
Publisher : STMIK KAPUTAMA

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Abstract

Lowongan Kerja adalah Sebuah Kesempatan kerja pada posisi tertentu di instansi atau tempat usaha. Kemudian menurut hasil   Survei  Angkatan   Kerja   Nasional BPS  Februari   2019, penganggur  di  usia  20-24  tahun  memiliki  proporsi  yang  paling besar , pada rentang usia tersebut para pencari kerja biasanya  baru  saja  lulus  dari  pendidikannya. Universitas Catur Insan Cendekia (CIC)  memiliki layanan sentra karir yang bertugas memberikan informasi tentang lowongan pekerjaan bagi alumni sebagaimana perguruan tinggi yang lain. Namun disentra karir Universitas Catur Insan Cendekia (CIC) belum memiliki sistem sentra karir yang menampung data informasi mengenai lowongan pekerjaan dan sistem untuk mengelola data alumni.  Salah satu solusi yang dapat menyelesaikan masalah tersebut adalah dengan membuat sebuah sistem sentra karir. Pada Penelitian ini membahas bagaimana menerapkan metode ROC dan MOORA dengan tujuh kriteria yang digunakan yaitu Pendidikan, Program Studi, IPK, Usia, Pengalaman, Skill dan Masa Berlaku dalam proses penentuan lowongan pekerjaan yang sesuai dengan kualifikasi alumni. Metode pengembangan perangkat lunak pada penelitian ini menggunakan Extreme Programming (XP) yang cepat dan dinamis terhadap perubahan. Analisis dan perancangan sistem menggunakan UML. Pembuatan perangkat lunak pada penelitian ini menggunakan bahasa pemrograman PHP dan framework CodeIgniter dan basis data MySQL. Hasil dari penelitian ini diapatkan nilai tertinggi dengan nilai 0,2000 untuk alternatif tiga (A3) yang memiliki nilai teratas yaitu pendidikan dengan enam kriteria penunjang lainnya. Dimana kriteria tersebut sesuai dengan kualifikasi yang dimiliki oleh alumni universitas CIC.  Berdasarkan hasil pengujian sistem, aplikasi sudah sesuai dengan analisis dan perancangan sistem.
Klasifikasi Minat Potensial calon Siswa-Siswi di Able Ballet Berdasarkan Profil Menggunakan Metode K-Nearest Neighbors (K-NN) Petrus Sokibi; Natalia Gunawan, Trivena; Lena Magdalena
JURNAL ILMIAH SAINS TEKNOLOGI DAN INFORMASI Vol. 2 No. 4 (2024): Oktober
Publisher : CV. ALIM'SPUBLISHING

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59024/jiti.v2i4.934

Abstract

Penelitian ini bertujuan untuk mengimplementasikan metode K-Nearest Neighbors (K-NN) dalam klasifikasi minat calon siswa-siswi di Able Ballet berdasarkan profil mereka. Metode K-NN digunakan karena kemampuannya dalam mengklasifikasikan data dengan tingkat akurasi yang tinggi. Penelitian dilakukan dengan mengumpulkan data profil siswa baru dan menggunakan metode K-NN untuk memprediksi minat mereka terhadap berbagai program yang ditawarkan. Hasil penelitian menunjukkan bahwa metode K-NN dapat mencapai tingkat akurasi sebesar 90% dalam klasifikasi minat siswa. Hal ini menunjukkan bahwa metode ini efektif dalam mendukung pengambilan keputusan untuk penempatan program yang sesuai bagi siswa baru. Penelitian ini juga menyarankan penggunaan dataset yang lebih besar dan eksplorasi metode lain seperti Support Vector Machine (SVM) untuk meningkatkan akurasi model di masa depan.
SISTEM PENUNJANG KEPUTUSAN DALAM PENENTUAN RUTE DAN KAPASITAS MUATAN DISTRIBUSI DENGAN MENGGUNAKAN METODE SAVING MATRIX DAN NEAREST NEIGHBOAR PADA CV BINTANG BERKAH CIREBON ., Nico Firmansyah; Lena Magdalena; Mesi Febima
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 10 No. 2 (2024): Volume 10 Nomor 2
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v10i2.3271

Abstract

This research creates and designs a Decision Support System (SPK) for CV Bintang Berkah by combining the Saving Matrix and Nearest Neighbor methods to optimize the route and capacity of the distribution load. CV Bintang Berkah faces challenges in managing distribution costs and load capacity, which affects operational efficiency. The Saving Matrix method helps in determining distribution routes with distance and cost savings, while the Nearest Neighbor method optimizes the order of visits based on the closest distance. The implementation of this system is expected to reduce transportation costs, increase distribution efficiency, and improve service quality. This system uses PHP and MySQL, focusing on the Cirebon City and Regency areas. The results show that applying this method significantly reduces the distance and cost of distribution and increases efficiency in the distribution process. With this system, CV Bintang Berkah is expected to overcome distribution challenges better, save costs, and increase the company's profitability.
SISTEM INFORMASI FORECASTING DATA PENJUALAN KENDARAAN MENGGUNAKAN METODE SINGLE EXPONENTIAL SMOOTHING (STUDI KASUS: PT. SENDANG SUMBER ARUM VIAR MOTOR CIREBON) Lestari, Lina; Lena Magdalena; Mesi Febima
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 10 No. 2 (2024): Volume 10 Nomor 2
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v10i2.3283

Abstract

Limited Liability Companies (PT) are found in almost all regions in Indonesia, one of which is PT.  Sendang Sumber Arum 'VIAR Motor which provides sales of Karya 3-wheeled motorcycle units, e-motorcycles, razors, Cross Adventure, vintech, and e-bikes. E-bikes are the best-selling vehicles for each period, especially the UNO3 type. The problem faced by this company is the imbalance in sales figures which causes damage to the e-bike batteries that are sold over a long period of time and requires forecasting. The Single Exponential Smoothing method is the right forecasting method used to predict demand for goods that change very quickly, which aims to determine the estimated availability of vehicle units that must be held in the future, based on previous sales data. In determining the error value in forecasting, the author uses the Mean Square Error (MSE) which is based on the alpha value. This forecasting is implemented into an information system that produces a forecast for the UNO3 type e-bike with the smallest Mean Square Error (MSE) value obtained with an alpha of 0.3, namely with a value of 167.294. This proves the best forecast for predicting the quantity of UNO3-type e-bike stock units at PT. Sendang Sumber Arum ‘VIAR Motor’ Cirebon for the period of June 2024 using alpha 0.3. So the forecast value of UNO3 type e-bike unit sales for June 2024 in the 11-month forecast period with alpha 0.3 is 24.89 or around 25 units with actual data.
Sistem Penunjang Keputusan Penilaian Kinerja Menggunakan Metode Fuzzy Mamdani untuk Menentukan Status Karyawan Lena Magdalena; Ridho Taufiq Subagio; Ida Wati
Prosiding SISFOTEK Vol 8 No 1 (2024): SISFOTEK VIII 2024
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

Employee performance appraisal is a performance management process in the company that functions as one of the decision-making for employee performance appraisal. At the Cirebon Non-Container Terminal PTP, a performance assessment has been carried out, but there are weaknesses, one of which is an input error in conducting an assessment which can directly change several components of the performance assessment so that the performance assessment decision is not accurate. The creation of a performance appraisal decision support system to determine the status of employees using the fuzzy mamdani method, produces a decision on employee performance appraisal recommendations and serves as a basis for consideration to change the status of employees who are still on contract to permanent employees. In this study, the tool used is Matlab (Matrix Laboratory) to compare the results graphically and the decision support system to be designed. The fuzzy mamdani method is applied with three assessment criteria, namely good, sufficient, and poor, this helps in making monthly decisions regarding the results of performance appraisals which are then recapitulated at the end of the year. The results of the calculation between Matlab and the Decision Support System using the fuzzy mamdani method show that the final score has a difference that is not much different, meaning that the fuzzy mamdani method has proven to be effective for employee performance assessment by producing accurate assessment decisions.
Implementasi Optimasi NLP dan KNN untuk User Review Aplikasi SAMPEAN Cirebon Mesi Febima; Lena Magdalena; Marsani Asfi; Muhammad Hatta; Rifqi Fahrudin
Prosiding SISFOTEK Vol 8 No 1 (2024): SISFOTEK VIII 2024
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

The use of information technology in the personnel administration process plays an active role in improving public services for civil servants (ASN) in the Cirebon City Government by providing accurate data for decision-making. One of the smart city applications that assists ASN in Cirebon City in supporting personnel administration activities is the SAMPEAN Cirebon City application. However, to ensure that this application is truly effective and meets user needs, it is important to analyze user reviews provided through application reviews. One effective method for analyzing user reviews is by using Natural Language Processing (NLP) and machine learning techniques. The NLP technique and classification model used is the KNN algorithm. The purpose of this research is to provide valuable input for application developers in improving the quality and performance of the SAMPEAN application. The research results show that by testing accuracy using the confusion matrix with K values of 3, 5, 7, and 9, it was found that K=9 provides the best performance with a balance between precision, recall, F1-Score, and accuracy. The model achieved a precision of 64%, recall of 90%, F1-Score of 75%, and accuracy of 62%. It can be concluded that with the optimization of the K parameter in KNN, the higher the K value, the higher the accuracy. This emphasizes the importance of selecting the right parameters to enhance the effectiveness of machine learning models in various Natural Language Processing (NLP) applications.
Implementation of the Haversine Formula Method in Geographic Information Systems for Searching the Nearest Sea Freight Expedition Services in East Jakarta Efriza Yunardi; Lena Magdalena; Mesi Febima
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 1 (2024): October 2024
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i1.622

Abstract

In export and import activities, a partner is needed for the shipping process. There are three types of shipping routes available for export and import: land, sea, and air. A company that offers shipping services via sea routes is known as an EMKL. This study designs a Geographic Information System (GIS) to facilitate the search for locations of Marine Cargo Expedition Services (EMKL) in East Jakarta using the Haversine Formula. The main problem faced is the difficulty in finding nearby and relevant expedition service providers in a large and densely populated area like East Jakarta. The proposed solution involves developing a system that integrates location data with the Haversine Formula to accurately calculate the distance between the user's location and the service providers. By using this method, the system can provide precise location information and help users select the nearest expedition service. The goal of this research is to enhance efficiency and accuracy in locating expedition services. The expected outcome is the creation of a user-friendly and effective system that simplifies the process of selecting expedition services by considering distance and service availability in real-time.
IMPLEMENTASI K-MEANS RFM DAN HOLT-WINTERS EXPONENTIAL SMOOTHING ADDITIVE DALAM SISTEM BUSINESS INTELLIGENCE UNTUK STRATEGI PENGELOLAAN PELANGGAN PADA PERUSAHAAN TRANSPORTASI.: Pembuatan Dashboard BI Segmentasi pelanggan dan peramalan Jumlah pelanggan menggunakan Tools Tableau menggunakan metode Kmeans RFM dan Holtwinters Exponential Smoothing Priandini, Belfania; Marsani Asfi; Lena Magdalena
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 11 No. 2 (2025): Volume 11 Nomor 2 Tahun 2025
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v11i2.4511

Abstract

The growth of customer data in the transportation industry drives the need for analytical systems capable of segmenting customers objectively and strategically. This study aims to apply the K-Means Clustering method based on the Recency, Frequency, and Monetary (RFM) model for customer segmentation and utilize the Holt-Winters Exponential Smoothing Additive method to forecast passenger numbers. The dataset comprises 10,440 customer transactions from PT XYZ during the 2022–2024 period. RFM values were calculated, normalized, and processed using the K-Means algorithm to produce three customer clusters: Loyal, Regular, and Passive. Subsequently, the Holt-Winters method was employed to forecast passenger numbers, achieving the smallest Mean Absolute Percentage Error (MAPE) of 6.88%, indicating a high level of accuracy. The results were visualized through an interactive dashboard using Tableau, enabling management to make data-driven decisions. This research demonstrates that integrating segmentation and forecasting methods into a Business Intelligence system can enhance the effectiveness of marketing strategies and the operational efficiency of the company.