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IMPLEMENTASI METODE WEIGHTED PRODUCT UNTUK PEMBERIAN BONUS KARYAWAN Subekti, Dayat; Wicaksono, Arief Ikhwan; Mukti, Agung Permana; Azzuhry, Dimas Rully
Jurnal Informatika Vol 8, No 1 (2024): JIKA (Jurnal Informatika)
Publisher : University of Muhammadiyah Tangerang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31000/jika.v8i1.9477

Giving bonuses to workers is an accomplishment that goes above and beyond the goal; it can raise productivity levels across the board for those involved. Employees frequently complain to business owners about differences in bonus calculations; these inaccuracies are frequently the result of incomplete data that was used to calculate and record data. The goal of this research is to apply the weighted product method algorithm to the decision support system for employee bonus distribution in order to promptly solve difficulties that arise. The MYSQL database, Django Framework, and Python programming language were used in the construction of the system. The weighted product method and the waterfall method are the two approaches used by the research road. The weighted product approach is used to determine bonuses, while the waterfall method is used for the phases of system development that include analysis, design, implementation, and testing. The study's findings will assist the finance department and business owners in organising data using preset criteria and performing calculations using the weighted product method to make employee bonus decisions easier.

Rancang Bangun Sistem Peringatan Dini Bencana Banjir Di Kabupaten Madiun Berbasis Website Dan SMS Gateway Menggunakan Mikrokontroller Arduino Nur Afandi, Halillur; Sudarmana, Landung; Subekti, Dayat; Alfi Sa'diya, Nafisa
Jurnal Teknomatika Vol 13 No 2 (2020): TEKNOMATIKA
Publisher : Fakultas Teknik dan Teknologi Informasi, Universitas Jenderal Achmad Yani Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30989/teknomatika.v13i2.1111

Madiun Regency is one of the districts on the island of Java, precisely in the province of East Java. Based on research from the Center for Volcanology and Geological Disaster Mitigation (PVMBG), Madiun City has several potential disasters, one of which is flooding with a high category. This is because Madiun Regency has two streams, namely Kali Sono and Kali Piring which head up on the slopes of Mount Wilis, Madiun Regency. Both times it ended in the Jerohan River in the Balerejo District, Madiun Regency which is a tributary of the Bengawan Madiun River. When heavy rains fell, parts of Madiun City were immediately inundated by flood waters. To anticipate floods, this research proposes a prototype design of a flood disaster early warning system using multiple sensors based on the Global System for Mobile Communications (GSM) in Madiun Regency. Which in the design uses the waterfall and Arduino methods. The prototype built is able to provide early warning information. The data that has been taken can be displayed on the web which functions as a monitoring system, so that officers can carry out supervision more easily and take action when the situation is dangerous. With the prototype and the system made, it is expected to be able to minimize losses caused by floods.

PERBANDINGAN METODE DECISION TREE DAN NAIVE BAYES CLASSIFIER PADA ANALISIS SENTIMEN PENGGUNA LAYANAN PT PERUSAHAAN LISTRIK NEGARA (PLN) BAGUS MUSTRIYANTO, ABIYOGA; Muhammad Habibi; Subekti, Dayat; Syahruddin, Fajar
Jurnal Teknomatika Vol 15 No 2 (2022): TEKNOMATIKA
Publisher : Fakultas Teknik dan Teknologi Informasi, Universitas Jenderal Achmad Yani Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30989/teknomatika.v15i2.1131

Background : PLN is a state-owned company that is tasked with supplying electricity to all regions of Indonesia which certainly cannot be separated from the various obstacles experienced, to find out public sentiment on the services that have been provided, an analysis is carried out to determine public sentiment. The results of these sentiments are created in the dashboard using the Flask framework by comparing the Naive Bayes and Decision tree methods. To create a sentiment analysis dashboard for PT. PLN and make a research analysis model using a comparison of the Naive Bayes Classification and Decision tree methods. The method used in this research is Naive Bayes and Decision tree. The data obtained with a total of 40,745 Tweet data taken in the period 1 May 2022 - 4 June 2022 with the keyword "PLN". Making a dashboard that displays the results of the analysis where there is a menu to display the data and each analysis process. The use of 900 training data and 300 testing data resulted in the Naive Bayes method getting an accuracy of 83% on the training data and 80% for the Testing data, while the Decision tree method got an accuracy of 77% on the Training data and 56% on the Testing data. The analysis obtained for the method in this study also shows that the Naive Bayes method is better for classifying large amounts of data than the Decision tree. The sentiment generated by the highest number is negative, with most of the Tweets being complaints about the response to complaints and handling of damage reported by the public.

OPTIMALISASI ALGORITMA RANDOM FOREST FEATURE SELECTION DAN HYPERPARAMETER TUNING KLASIFIKASI GENRE MUSIK Fakhriza, Fathur; Subekti, Dayat; Cahyo, Puji Winar
Jurnal Informatika Vol 9, No 1 (2025): JIKA (Jurnal Informatika)
Publisher : University of Muhammadiyah Tangerang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31000/jika.v9i1.12216

Mendengarkan musik merupakan aspek penting dari kehidupan manusia, namun pengenalan genre musik secara subjektif menambah kompleksitas dalam proses klasifikasinya. Oleh karena itu, diperlukan pendekatan yang teliti dan andal untuk menganalisis serta mengelompokkan data musik. Metode Random Forest banyak digunakan dalam klasifikasi genre musik, memerlukan optimalisasi algoritma yang presisi melalui Feature Selection dan Hyperparameter Tuning. Manfaat penelitian ini yaitu untuk memberikan pemahaman mengenai peran teknik Feature Selection dan Hyperparameter Tuning dalam mengoptimalkan performa algoritma Random Forest. Dengan memanfaatkan algoritma secara maksimal, akurasi klasifikasi genre musik dapat ditingkatkan, yang berperan penting dalam menciptakan sistem rekomendasi musik yang lebih tepat dan akurat. Penelitian ini diawali dengan pengumpulan data yang diolah dalam proses preprocessing untuk mendapatkan data yang bersih. Fitur-fitur dalam dataset dipilih melalui Feature Selection untuk mendapatkan fitur yang mampu merepresentasikan kelas genre musik. Metode Random Forest digunakan untuk klasifikasi, diikuti dengan Hyperparameter Tuning untuk mendapatkan parameter yang optimal. Hasil pengujian menunjukkan bahwa metode Random Forest memiliki nilai ROC AUC sebesar 0.909. Optimalisasi meningkatkan kinerja dengan nilai ROC AUC menjadi 0.913, menunjukkan peningkatan kinerja model sebesar 0.004 dan masuk kategori evaluasi yang excellent

EXPERT SYSTEM FOR DIAGNOSIS OF AIRBORNE INFECTIOUS DISEASES IN HUMANS WITH MAMDANI FUZZY LOGIC Subekti, Dayat; Priyanto, Agung; Mukti, Agung Permana; Azzuhry, Dimas Rully
JUTEKIN (Jurnal Teknik Informatika) Vol 11, No 1 (2023): JUTEKIN
Publisher : LPPM STMIK DCI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51530/jutekin.v11i1.671

People in today's information age are increasingly demanding all kinds of information to be presented quickly. One of them is information in the medical field, in this case information about the diagnosis of a disease which is usually in the form of an expert system. Various methods are used in expert systems, one of which is Mamdani fuzzy logic or better known as the Mamdani inference system. The use of fuzzy logic in diagnosing this disease does not require numbers as input.The Mamdani fuzzy logic expert system in this study is specifically used to diagnose infectious diseases in humans, namely: whooping cough or pertussis, measles, diphtheria, mumps, meningitis, tuberculosis, variola, and varicella. This system diagnoses diseases based on inputting the characteristics or symptoms suffered. The diagnosis result is a number of possible diagnoses for each disease. The closer the number to 1 (one), the higher the probability of contracting one of the diseases mentioned above.The system that has been created can already be used to detect diseases according to the characteristics or symptoms entered. However, this system is only a research and the results cannot be used as a reference for real disease diagnosis.

Pengondisian Sinyal ADXL203 sebagai Sensor Intensitas Getaran Dalam WSN dan IOT Priyanto, Agung; Rahmawati, Titik; Subekti, Dayat
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 9 No. 1 : Tahun 2024
Publisher : LPPM UNIKA Santo Thomas

Show Abstract | Download Original | Original Source | Check in Google Scholar

Wireless Sensor Network (WSN) dan Internet of Things (IoT) merupakan teknologi yang tidak dapat dipisahkan dalam kehidupan sehari-hari di masa sekarang. Penerapannya begitu luas mulai dari peranti paling dekat dengan manusia seperti alat pemantau kesehatan nirkabel sampai dengan peranti untuk mitigasi bencana seperti tanah longsor, banjir dan lain-lain. Dalam perancangannya, banyak ditemui ketakcocokan level tegangan keluaran sensor dengan port masukan perangkat WSN ataupun IoT. Selain ketakcocokan level tegangan, sering kali dibutuhkan sebuah rangkaian pengondisi sinyal tambahan sehingga keluarannya sesuai dengan peruntukannya. Salah satu dari sensor-sensor tersebut adalah sensor ADXL203, sebuah akselerometer yang dapat difungsikan sebagai sensor getaran, sensor perpindahan maupun sensor kemiringan. Tulisan ini akan membahas mengenai perancangan instrumen pengondisi sinyal dan pemrogramannya untuk keluaran ADXL203 yang digunakan sebagai sensor intensitas getaran pada WSN maupun IoT. Hasil akhir yang diperoleh adalah sebuah sensor intensitas getaran yang terintegrasi dengan WSN platform IQRF yang siap digunakan untuk berbagai keperluan.

Pengondisian Sinyal ADXL203 sebagai Sensor Intensitas Getaran Dalam WSN dan IOT Priyanto, Agung; Rahmawati, Titik; Subekti, Dayat
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 9 No. 1 : Tahun 2024
Publisher : LPPM UNIKA Santo Thomas

Show Abstract | Download Original | Original Source | Check in Google Scholar

Wireless Sensor Network (WSN) dan Internet of Things (IoT) merupakan teknologi yang tidak dapat dipisahkan dalam kehidupan sehari-hari di masa sekarang. Penerapannya begitu luas mulai dari peranti paling dekat dengan manusia seperti alat pemantau kesehatan nirkabel sampai dengan peranti untuk mitigasi bencana seperti tanah longsor, banjir dan lain-lain. Dalam perancangannya, banyak ditemui ketakcocokan level tegangan keluaran sensor dengan port masukan perangkat WSN ataupun IoT. Selain ketakcocokan level tegangan, sering kali dibutuhkan sebuah rangkaian pengondisi sinyal tambahan sehingga keluarannya sesuai dengan peruntukannya. Salah satu dari sensor-sensor tersebut adalah sensor ADXL203, sebuah akselerometer yang dapat difungsikan sebagai sensor getaran, sensor perpindahan maupun sensor kemiringan. Tulisan ini akan membahas mengenai perancangan instrumen pengondisi sinyal dan pemrogramannya untuk keluaran ADXL203 yang digunakan sebagai sensor intensitas getaran pada WSN maupun IoT. Hasil akhir yang diperoleh adalah sebuah sensor intensitas getaran yang terintegrasi dengan WSN platform IQRF yang siap digunakan untuk berbagai keperluan.

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