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All Journal Jurnal Pilar Nusa Mandiri INTI Nusa Mandiri Jurnal Sistem Informasi Bulletin of Computer Science Research Akasia: Artikel Ilmiah Sistem Informasi Akuntansi Computer Science (CO-SCIENCE)
Ainun Zumarniansyah
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Computer Science & IT

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Penerapan Sistem Pendukung Keputusan Penilaian Karyawan Terbaik Dengan Metode Simple Additive Weighting Ainun Zumarniansyah; Rian Ardianto; Yuris Alkhalifi; Qudsiah Nur Azizah
Jurnal Sistem Informasi Vol 10 No 2 (2021): JSI Periode Agustus 2021
Publisher : LPPM STMIK ANTAR BANGSA

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (733.397 KB) | DOI: 10.51998/jsi.v10i2.419

Abstract

Intisari— Dalam Penilaian Karyawan Terbaik pada PT. Berkah Jaya Motor, ada beberapa faktor yang menjadi penilaian dan berdasarkan penilaian kinerja karyawan diperusahaan. Penilaian karyawan di PT. Berkah Jaya Motor masih mengalami kendala karena masih menggunakan sistem Penilaian dengan cara Perundingan. Demi efisiensi kerja maka pengambilan keputusan yang tepat sangat diperlukan. Dengan tujuan untuk membangun dan memberikan alternatif. Untuk Penilaian karyawan terbaik dengan menggunakan metode Simple Additive Weighting (SAW). dimana ada beberapa kriteria yang masing-masing memiliki bobot penilaian sehingga memberikan hasil penilaian karyawan yang akurat terhadap setiap kinerja karyawan terbaik. Hasil akhir diperoleh dari proses perhitungan, yaitu penjumlahan dari matriks ternormalisasi dengan bobot per kriteria yang menunjukan rangking pemilihan karyawan terbaik dari pertama hingga yang terakhir dari kriteria. Dari penilaian tersebutlah menjadi alternatif yang kemudian mendapat Karyawan Terbaik. Kata Kunci— Sistem Pendukung Keputusan, Karyawan Terbaik, Simple Additive Weighting Referensi : [1]  A. G. Anto, H. Mustafidah, and A. Suyadi, “Sistem Pendukung Keputusan Penilaian Kinerja Karyawan Menggunakan Metode SAW (Simple Additive Weighting) di Universitas Muhammadiyah Purwokerto,” JUITA, vol. 4, pp. 193–200, 2015, Accessed: Jun. 18, 2021.[Online]. Available: http://www.jurnalnasional.ump.ac.id/index.php/JUITA/article/view/876 [2]  A. T. Widiyanto and Y. Erliani, “Sistem Pendukung Keputusan Dalam Menentukan Karyawan Terbaik Pada PTt. Tembaga Mulia Semanan Dengan Metode Topsis,” 2016. [3]  I. Pratama, Sistem Informasi dan Implementasinya. 2019. [4]  D. I. Sabanayo, “Sistem Pendukung Keputusan Pemilihan Karyawan Terbaik Menggunakan Metode SAW Pada PT. Berkah Cahaya Muria Kudus,” 2015. [5]  T. Syahputra, M. Yetri, and S. D. Armaya, “Sistem Pengambilan Keputusan Dalam Menentukan Kualitas Pemasukan Pangan Segar Metode Smart,” JURTEKSI, vol. 04, no. 01, 2017, Accessed: Jun. 18, 2021. [Online]. Available: https://jurnal.stmikroyal.ac.id/index.php/jurteksi/article/view/19/18 [6]  I. Fahmi, Teori dan Teknik Pengambilan Keputusan Kualitatif dan Kuantitatif . 2016. [7]  S. Mallu, “Sistem Pendukung Keputusan Penentuan Karyawan Kontrak Menjadi Karyawan Tetap Menggunakan Metode TOPSIS,” JITTER, vol. 01, no. 02, 2015, Accessed: Jun. 18, 2021. [Online]. Available: http://journal.widyatama.ac.id/index.php/jitter/article/view/53 . 2021 [8] K. Safitri, F. Tinus Waruwu, and Mesran, “Sistem Pendukung Keputusan Pemilihan Karyawan Berprestasi Dengan Menggunakan Metode Analytical Hieararchy Process Process Studi Case PT.Capella Dinamik Nusantara Takengon vol. 1, no. 1, pp. 12–16, 2017, Accessed: Jun. 18,  ,” vol. 1, no. 1, pp. 12–16, 2017, Accessed: Jun. 18, 2021. [Online]. Available: https://ejurnal.stmik budidarma.ac.id/index.php/mib/article/view/317/268 [9]  G. Taufiq, “Implementasi Logika Fuzzy Tahani Untuk Model Sistem Pendukung Keputusan Evaluasi Kinerja Karyawan,” Jurnal Pilar Nusa Mandiri, vol. XII, no. 1, 2016, Accessed: Jun. 18, 2021. [Online]. Available: http://ejournal.nusamandiri.ac.id/index.php/pilar/article/view/254/224 [10] D. Nofriansyah, Konsep Data Mining Vs Sistem Pendukung Keputusan, I. Yogyakarta: Deepublish, 2014. [11] D. Fatihudin, Metode Penelitian untuk Ekonomi, Manajemen dan Akuntansi. 2015. [12]  J. Hartono, Analisis & Desain Sistem Informasi Pendekatan Terstruktur Teori Dan Praktik Aplikasi Bisnis. 2014. [13]  I. G. B. Subawa, I. M. A. Wirawan, and I. M. G. Sunarya, “Pengembangan Sistem Pendukung Keputusan Pemilihan Pegawai Terbaik Menggunakan Metode Simple Additive Weighting (SAW) Di PT Tirta Jaya Abadi Singaraja,” Karmapati, vol. 4, no. 5, 2015, Accessed: Jun. 18, 2021. [Online]. Available: https://ejournal.undiksha.ac.id/index.php/KP/article/view/6623/4511
Metode Design Thinking Pada Sistem Informasi Presensi Pegawai Kejaksaan Negeri Kota Bogor Yuris Alkhalifi; Khairul Rizal; Amir Amir; Ainun Zumarniansyah; Destia Sari Rahmadhani Fadillah
Computer Science (CO-SCIENCE) Vol. 3 No. 2 (2023): Juli 2023
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v3i2.1968

Abstract

Employee presence is an important factor for an agency or company to achieve goals, this is related to discipline and has an impact on the performance of each employee. But in reality, there are still several agencies that still use manual absences that have not been computerized, one of which is the Kejaksaan Negeri Kota Bogor's Office. The system used by the Kejaksaan Negeri Kota Bogor in the attendance process is still manual, namely using a daily attendance book which affects the efficiency and effectiveness of data collection, data search, and calculations and requires a relatively long time. Therefore, it is necessary to have special data collection to record attendance and absence so that work activities can be recorded in real-time and properly, one of which is by using a computerized system with Information Systems. The information system built on a website basis uses the CodeIgniter 3 framework and MySQL. The method used is the Design Thinking Method which has 5 stages. The output of this study is known to be tested with usability testing by user on the Learnability aspect of 75%, then on the efficiency aspect of 100% and the memorable aspect of 66.77%. The average result of the test is quite good at 80.56%. So by making this information system, it can facilitate the process of being on time, searching for data, calculating and summarizing timeliness and minimizing errors when recording presence data.
TWITTER SENTIMENT ANALYSIS OF POST NATURAL DISASTERS USING COMPARATIVE CLASSIFICATION ALGORITHM SUPPORT VECTOR MACHINE AND NAÏVE BAYES Zumarniansyah, Ainun; Pebrianto, Rangga; Normah, Normah; Gata, Windu
Jurnal Pilar Nusa Mandiri Vol 16 No 2 (2020): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v16i2.1423

Abstract

Natural disasters trigger people, especially Twitter users to provide information or opinions in the form of tweets. The Tweet can be an expression of sadness, concern, or complaint. Processing of data from these tweets will create trends that can be used for information needs such as education, economics, and others. Natural disasters are events that threaten human life caused by nature, including in the form of earthquakes. The method used is the Support Vector Machine and Naive Bayes from the tweet. The data collected is filtered from tweets by deleting duplicate data. In calculating the Natural Disaster sentiment analysis using a comparison of the Support Vector Machine and the Naive Bayes algorithm, the difference in accuracy is 3.07% where the results of the Support Vector Machine are greater than Naive Bayes. The purpose of this research is to analyze sentiment for the distribution of disaster aid that does not flow information due to information & coordination in the field. so as to provide information on the location of natural disasters, natural disaster management, and its presentation to victims that can be shared evenly in an efficient time due to information and natural management so that the distribution of aid is hampered
PENERAPAN DECISION TREE DENGAN PENYEIMBANGAN DATA IMBALANCE MENGGUNAKAN UPSAMPLING DALAM PREDIKSI PENYAKIT LIVER Agung Fazriansyah; Yuris Alkhalifi; Ainun Zumarniansyah
INTI Nusa Mandiri Vol. 19 No. 2 (2025): INTI Periode Februari 2025
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v19i2.6369

Abstract

Acute liver disease has a significant impact on liver function and is often only detected at an advanced stage due to the lack of patient awareness for early examination.  One of the challenges in treating liver disease is the delay in diagnosis, where many patients do not notice the early symptoms until their condition has worsened.  Therefore, a predictive system is needed that can identify liver disease patients early on, allowing for regular check-ups and timely treatment.  In this study, a classification model was developed using a machine learning approach, specifically the Decision Tree algorithm, by balancing the data in the minority class through upsampling.  The research results show that this model is capable of predicting liver disease status with an accuracy rate of 89.22%, a recall of 88.45%, a precision of 83.21%, and an f1-score of 85.78%.  In addition, the ROC-AUC value of 0.89 is categorized as a good classification.  This model achieved a higher accuracy score than other studies with similar datasets.  This system is expected to help improve early detection and expedite the treatment of liver disease patients.
Analisis Sentimen Berita Online Terhadap Transportasi Online di Indonesia dengan Metode Naïve Bayes Classifier, Support Vector Machine dan K-Nearest Neighbor Selawati, Arina; Yan Rianto; Rachmawati Darma Astuti; Ainun Zumarniansyah; Deny Novianti
Bulletin of Computer Science Research Vol. 5 No. 2 (2025): February 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i2.477

Abstract

News about online transportation in Indonesia in 2019 until early 2020 has been published in various Indonesian online media, because there is enough information in the form of text without numerical scale, it is difficult to classify information information efficiently without reading the full text. Sentiment analysis is used to automate the process of assessing opinion whether it is positive or negative. Classifying sentiments on news from online news media with the Text Mining process and using the method of increasing the Classification Accuracy / Ensemble Method of Engineering by combining the classification algorithm naïve bayes method, classifier Supporting vector machines and k-nearest neighbors added with the Particle Swarm Optimization method and Vote method The next will be a comparative analysis. The results of the study above get an SVM exam accuracy value even after using the PSO selection feature with the ensemble. Select is still appropriate at 84.16%, Likewise for NB algorithm which gets 79.08% and KNN which gets approval 87.19%. These words will be used to see words related to sentiments that often appear and have the highest weight and can be used to find out positive news articles and negative news articles. And for this research the model that uses KNN algorithm gets the highest accuracy.
Penerapan Metode Prototype Rancang Bangun Sistem Informasi Penjualan Mobil Bekas Kredit Pada Mobilindo Pratama Ainun Zumarniansyah; Cahya, Fani Nurona; Pebrianto, Rangga
Akasia: Artikel Ilmiah Sistem Informasi Akuntansi Vol 5 No 1 (2025): Artikel Ilmiah Sistem Informasi Akuntansi (AKASIA) - April 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/sqf0e755

Abstract

Seiring dengan meningkatnya permintaan kendaraan bermotor, terutama mobil bekas yang dibeli secara kredit, kebutuhan akan sistem informasi yang terstruktur dan efisien menjadi sangat penting. Mobilindo Pratama, sebagai salah satu showroom mobil bekas, masih menggunakan sistem manual dalam pencatatan data dan transaksi, yang berdampak pada keterlambatan pelayanan, risiko kesalahan pencatatan, serta ketidakefisienan dalam pembuatan laporan. Penelitian ini bertujuan untuk merancang dan membangun sistem informasi penjualan mobil bekas berbasis komputer dengan menggunakan metode prototype. Metode ini memungkinkan pengguna untuk berinteraksi langsung dengan sistem pada tahap awal, sehingga pengembang dapat memperbaiki dan menyesuaikan sistem berdasarkan masukan yang diberikan. Sistem yang dibangun mencakup fitur pemesanan kendaraan, pengajuan kredit, pelunasan uang muka, pencatatan transaksi, dan pembuatan laporan penjualan. Hasil implementasi menunjukkan bahwa sistem ini dapat membantu mempercepat proses penjualan, meningkatkan akurasi pencatatan data, serta memberikan kemudahan dalam pelaporan. Dengan demikian, sistem informasi ini diharapkan dapat meningkatkan kinerja operasional dan kualitas pelayanan di Mobilindo Pratama secara keseluruhan.
Co-Authors Agung Fazriansyah Amir Amir Arina Selawati Cahya, Fani Nurona Deny Novianti Destia Sari Rahmadhani Fadillah Gata, Windu khairul rizal Normah Normah, Normah Qudsiah Nur Azizah Rachmawati Darma Astuti Rian Ardianto Yan Rianto Yuris Alkhalifi