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All Journal Gaung Informatika Jurnal Nasional Teknologi Informasi dan Aplikasinya Jimstek
Dwi Retnoningsih
universitas Sahid Surakarta
Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Veterinary

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SISTEM PENGELOLAAN KENDARAAN DINAS DI PEMERINTAH KOTA SALATIGA Alfiyanus Shaf’at; Dwi Retnoningsih; Hardika Khusnuliawati
JURNAL GAUNG INFORMATIKA Vol 13 No 2 (2020): Jurnal Gaung Informatika, Volume 13, Nomor 2 Juli 2020,
Publisher : Universitas Sahid Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47942/gi.v13i2.539

Abstract

The optimal management of regional property has become a basic requirement for local governments in realizing good governance. Official vehicles as one part of regional property have a significant function. However, the process of managing official vehicles in regional organizations of Salatiga government is still not optimal. For example, the frequency of vehicle maintenance schedules are missed and vehicle tax payments are due. The change of official vehicle users also often occurs and it will be a problem if the process is not recorded properly. This study aims to design an applications in supporting regional organizations at Salatiga government for managing data and presenting information related to official vehicles. This study used the Linear Sequential Model / Waterfall Model. The data collection covered observation, interviews and literature study. The application wasmade in the form of a desktop application using the Node.js platform and it is based on javascript and utilizes the electron framework. The database used SQLite. Analysis and design of the system used a structured method through the stages of making normalization, relations between tables, entity relationship diagrams, data dictionaries, data flow diagrams, flowcharts, and interface design. The resulting application is tested using the blackbox testing method. The analysis of test results shows that the application is functioning normally according to the expected results. The main function of managing data on the use and maintenance of official vehicles has been successful through all the test scenarios conducted.
Implementasi Metode LSTM untuk Prediksi Harga Saham PT Indofood CBP Sukses Makmur TBK Endritha Pramudya; Dwi Retnoningsih; Diyah Ruswanti
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 3 No. 4 (2025): JNATIA Vol. 3, No. 4, Agustus 2025
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2025.v03.i04.p24

Abstract

High stock price fluctuations make stock prices difficult to predict accurately. Therefore, a predictive analysis approach that utilizes historical data in addition to machine learning methods is needed to help estimate price movements more effectively. This study aims to determine the performance of the Long Short-Term Memory (LSTM) method in predicting the stock prices of PT Indofood CBP Sukses Makmur Tbk based on historical data. LSTM is a type of artificial neural network that is effective in processing time series data due to its ability to capture long term relationships between data. Historical data is used to train the LSTM model. The results show that the LSTM model is effective in predicting stock prices, with an average accuracy of 80.5%. Sukses Makmur Tbk based on historical data. LSTM is one type of artificial neural network that is effective in processing time series data due to its ability to capture long-term relationships between data points. The data used consists of ICBP stock closing prices from January 2019 to May 2025. The methods used include data cleaning, data normalization, data splitting, model design, prediction, denormalization, and evaluation using the Mean Absolute Percentage Error (MAPE) metric. The research results demonstrate that the LSTM model performs well in recognizing time series data patterns, as indicated by the lowest MAPE value of 1.43, at the combination of 100 epochs and a batch size of 32.
RANCANG BANGUN KUNCI OTOMATIS MENGGUNAKAN POLA KETUKAN BERBASIS KETUKAN BERBASIS ARDUINO Andri Budi Laksono; Diyah Ruswanti; Dwi Retnoningsih
JIMSTEK: Jurnal Ilmiah Sains dan Teknologi Vol. 3 (2021): JIMSTEK
Publisher : Fakultas Sains, Teknologi dan Kesehatan Universitas Sahid Surakarta

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Abstract

The development of technology and completeness of electronic equipment do not cause changes in society. It is indicated by the fact that the community still uses a simple key sistem. In opening and closing the door, they still use conventional keys such as child locks and sliding locks. This type of locking has been applied as a general locking method. However , the obstacles that occur in manual locking are the difficulty in opening the lock, for example, a jammed lock that requires extra effort to open. The use of manual locks is easy to duplicate, is prone to lose if taken away, and can allow other people to enter private homes or rooms. Therefore we need a door locking system that can provide ease of use from conventional locking, namely using automatic locking with a knock pattern. This research aims to design an automatic lock using an Arduino – based knock pattern, which provides another alternative in locking the door. This study uses a trial and error method, where the tool will continue to be tested until it is successfully implemented. This study uses a piezoelectric as a reader of vibrations resulting from a given beat. Furthermore, the given vibration will be stored by the system as a password to unlock the door. Relays are used to cut or supply voltage to the solenoid. Red led and green led is used as user indicators. Push – button is used to rest when entering a new password on the system. The result of this research is that the automatic unlocking system can identify the number of beats. If the number of beats detected is less than or equal to 20 (twenty), then the beats are correct, and the beats are wrong if the number of beats is more that 20 (twenty). The automatic unlocking system can also identift the slow and loud knocks when you want to unlock the door. The sound produced from a knock recorded using a smartphone device cannot be used as a door opener or cannot be used as an access control method. It is since the recorded knock sound does not provide sufficient vibration to be identified as a beat by the system.
Sistem Pendukung Keputusan Penilaian Kinerja Pegawai Dpmptsp Kota Salatiga Menggunakan Moora (Multi-Objective Optimization By Ratio Analysis) Nourmalina Ayu Sekarini; Dwi Retnoningsih; Astri Carolina
JIMSTEK: Jurnal Ilmiah Sains dan Teknologi Vol. 6 No. 01 (2024): JIMSTEK: Jurnal Ilmiah Sains dan Teknologi
Publisher : Fakultas Sains, Teknologi dan Kesehatan Universitas Sahid Surakarta

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Abstract

Proses penilaian kinerja pegawai pada DPMPTSP Kota Salatiga dilakukan secara manual dengan menggunakan form yang sudah ditetapkan dengan kriteria penilaian kinerja yaitu nilai SKP, orientasi pelayanan, integritas, komitmen, disiplin, kerjasama, dan kepemimpinan. Pelaksanaan penilaian kinerja pegawai mengalami kendala untuk penentuan pegawai yang mendapatkan penghargaan. Mengingat jumlah pegawai yang banyak dan proses evaluasi kinerja pegawai yang cukup rumit dengan banyaknya kriteria menyebabkan beberapa kali terjadi kesalahan dalam pencatatan data evaluasi kerja dan proses perhitungan hasil evaluasi kerja. Selain itu, banyaknya pegawai yang dievaluasi menyebabkan proses pengelolaan hasil evaluasi kerja membutuhkan waktu yang cukup lama, hal ini tentunya dinilai tidak efisien. Penelitian ini bertujuan untuk membuat sistem pendukung keputusan yang dapat memperhitungkan segala kriteria guna mempercepat dan mempermudah penilaian kinerja pegawai yang lebih efektif pada DPMPTSP Kota Salatiga dengan menggunakan metode MOORA. Penelitian ini menggunakan 5 kriteria penilaian yaitu pelayanan dengan bobot nilai 0,25, integritas dengan bobot nilai 0,20, komitmen dengan bobot nilai 0,15, disiplin dengan bobot nilai 0,15, kerjasama dengan bobot nilai 0,15 dan kepemimpinan dengan bobot nilai 0,10. Hasil penilaian kinerja menggunakan metode MOORA dari penilaian kinerja pegawai DPMPTSP Kota Salatiga diperoleh pegawai terbaik yaitu RIAWAN WIDYATMOKO, S.P dengan nilai 0,191
Penggunaan Metode Rank Order Centroid dalam Penentuan Nilai Centroid (Studi Kasus : Dataset Biji Gandum) ricky dwi saputro Saputro; Dwi Retnoningsih; Hardika Khusnuliawati
JIMSTEK: Jurnal Ilmiah Sains dan Teknologi Vol. 6 No. 01 (2024): JIMSTEK: Jurnal Ilmiah Sains dan Teknologi
Publisher : Fakultas Sains, Teknologi dan Kesehatan Universitas Sahid Surakarta

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Abstract

Clustering is a data processing in data mining, namely a clustering process by separating a set of data into smaller groups or clusters based on similar characteristics. A clustering method in data mining is K-Means. The KMeans method processes data into the desired number of clusters and the data will be placed into clusters based on the proximity of the centroid or distance to each cluster. The conventional K-Means method has the disadvantage, namely the initial centroid selection is random so it can produce different results. Therefore, initial centroid selection can improve accuracy in the clustering process. Determining the initial centroid can be done in various ways such as Rank Order Centroid (ROC). ROC is a method that gives a score to each criterion according to its ranking appropriate to score based on its priority level. This research compares the evaluation results with the Davies Bouldin Index (DBI) method, which is a cluster validation method for grouping methods. The results of the clustering method using wheat grain data show that a DBI KMeans value using ROC of 0.33350 with 4 iterations in 2 clusters. Keywords : Clustering, K-Means, centroid, ROC
Co-Authors Alfiyanus Shaf’at Andri Budi Laksono Astri Carolina Endritha Pramudya Hardika Khusnuliawati, Hardika Nourmalina Ayu Sekarini ricky dwi saputro Saputro Ruswanti, Diyah