Multidisciplinary Applications of Quantum Information Science (Al-Mantiq)

Sistem Pendukung Keputusan Pengangkatan Pegawai Tetap Pada Lembaga Pendidikan Ma’arif Nu Cabang Bojonegoro Menggunakan Metode Topsis

2024-08-01T02:07:04+00:00

Pegawai menjadi elemen kunci dalam kesuksesan organisasi, termasuk Lembaga Pendidikan Ma'arif NU Cabang Bojonegoro. Pengangkatan pegawai tetap menjadi tahap penting dalam memastikan kualitas sumber daya manusia yang mendukung visi dan misi lembaga. Penelitian ini mengembangkan Sistem Pendukung Keputusan (SPK) berbasis komputer menggunakan metode Technique Order Preference by Similarity To Ideal Solution (TOPSIS) untuk mempercepat dan meningkatkan akurasi pengangkatan pegawai tetap di Lembaga Pendidikan Ma'arif NU Cabang Bojonegoro. Proses manual yang ada telah mengakibatkan ketidakpastian data dan kebutuhan akan sistem yang lebih efisien. Dengan SPK berbasis web, seleksi pegawai dapat dipercepat, meningkatkan produktivitas sesuai kriteria yang ditentukan. Hasil penelitian menunjukkan keberhasilan implementasi TOPSIS dalam pengujian blackbox, dengan sistem yang layak dan sesuai dengan fungsi yang diharapkan. Dengan persentase 94% dalam pengujian kelayakan, sistem ini diharapkan dapat meningkatkan efisiensi dan efektivitas pengangkatan pegawai tetap di lembaga tersebut.

IMPLEMENTASI METODE K-MEANS CLUSTERING UNTUK MENENTUKAN PERSEDIAAN STOK BARANG PADA TOKO AT-THULLAB TUBAN

2024-08-01T02:01:38+00:00

The At-Thullab store is an office stationery (ATK) store that sells various kinds of office stationery needs and several other types of goods such as pens, books, pencils, children's toys, and some birthday supplies. The At-Thullab store sells a variety of stationery among the community in the Sumurcinde Village area, Soko District, Tuban Regency. In handling stationery sales services, the At-Thullab store has one employee to serve the needs of its own buyers. In sales transactions there are many buyers who make transactions every month. There are so many types of goods it is not known which goods are most interested in buyers, so sometimes there are stockpiles of goods that are of less interest to buyers. In this study, k-means clustering was applied in determining clustering on item data in the At-Thullab store . Determination of inventory stock that can be accessed through web-based k-means clustering can be used as a support or complement for shop owners, shop heads and store employees, at At-Thullab stores. With these results, the store can also consider which products will be introduced to the store with the most sales. In this study, it can be concluded that several things are needed to analyze sales that occurred at the At-Thullab Store on January 1 – March 20 2023. The data was processed using k-mean clustering to determine groups of goods that did not sell well, sold and those that were very salable in this study the.

Sistem Identifikasi Pencemaran Air Sungai Berbasis Internet Of Things

2024-07-19T19:08:12+00:00

Rivers are open-water ecosystems that are vulnerable to pollution or damage. Pollution that occurs in rivers is usually caused by environmental conditions and human activity that settles around the river. Water ecosystems consist of interrelated biotic and abiotic components; when both components are interrupted, changes in the ecosystem become unbalanced. Water pollution can be caused either intentionally or accidentally, but the main factor in the occurrence of water contamination that is often found is the result of human activity. Technology in this era is evolving very fast. Therefore, there are many prototypes that can support the development of such technologies, such as node-MCU and the Internet of Things (IOT). NodeMCU is a microcontroller equipped with the WiFi module ESP8266. NodeMCU also has a relatively cheaper price.The test results on this system were obtained by conducting a black box test and a validity test on an IOT-based river water pollution identification system using a pH sensor and a turbidity sensor. The results of testing the application of fuzzy sugeno produce a value of 100% from the compatibility of the 3 tests, namely testing on the system, matlab and manual calculations.He suggested that this system could make it easier to know the quality of contaminated or uncontaminated river water.

Prediksi Tingkat Kelahiran Bayi di Kabupaten Bojonegoro dengan Menggunakan Algoritma Naive Bayes

2024-08-01T02:17:32+00:00

Perkembangan teknologi informasi pada zaman ini yang sangat pesat dimulai dari penemuan informasi baru pada big data dengan mencari suatu pola tertentu atau sering disebut dengan istilah Data Mining. Data mining merupakan proses dimana menggunakan teknik statistik, matematika, kecerdasan buatan, serta machine learning yang digunakan untuk mengekstraksi (proses pemisahan) dan mengidentifikasi informasi yang bermanfaat terkait dari berbagai database besar. Dengan berkembangnya teknologi informasi saat ini, terutama pada bidang data mining yang telah banyak digunakan dalam sistem informasi. Ada beberapa metode klasifikasi data mining yang sering digunakan untuk memprediksi atau meramalkan, seperti Algoritma Naive Bayes Classifier, Decision Tree, Neural Network, K-Nearest Neighbour, Artificial Neural Network, dan lain metode klasifikasi lain sebagainya. Metode yang digunakan pada penelitian ini menggunakan metode Algoritma Naive Bayes yang memanfaatkan metode probabilitas dan statistik yang dikemukakan oleh ilmuan Inggris Thomas Bayes, yaitu ilmuan yang memprediksi probabilitas di masa depan berdasarkan dengan pengalaman di masa sebelumnya atau masa lalu. Faktor-faktor yang mempengaruhi angka kelahiran sendiri yaitu faktor jenis kelamin laki-laki, jenis kelamin perempuan, dan pasangan usia subur. Angka kelahiran bayi sebagai indikator yang penting untuk mencerminkan keadaan derajat kesehatan di suatu masyarakat. Data yang digunakan dari tahun 1993 sampai dengan tahun 2022. Hasil analisis Algoritma Naive Bayes pada penelitian ini yaitu sebanyak 27 data diprediksi rendah dan 3 data diprediksi rendah pada tahun 2017, 2020, 2021 serta mendapatkan nilai akurasi (accuracy), Recall dan Precision sebesar 100%

IMPLEMENTASI NAÏVE BAYES CLASSIFIER UNTUK ANALISIS SENTIMEN APLIKASI PINJAMAN ONLINE PADA MEDIA SOSIAL TWITTER

2024-08-01T02:04:33+00:00

Sistem analisis sentimen adalah sistem yang digunakan untuk melakukan proses analisis otomatis pada tweet aplikasi pinjaman online berbahasa Indonesia untuk mendapatkan informasi termasuk informasi sentimen yang menjadi bagian dari tweet tersebut. Data tersebut diklasifikasikan dengan menggunakan Naive Bayes. Sistem analisis sentimen dibagi menjadi 5 (lima) tahap, yaitu scraping, pre-processing, pembobotan kata, pembangunan model dan klasifikasi sentimen. Metode TF-IDF (Term Frequency - Inverse Document Frequency) digunakan untuk pembobotan kata. Data yang ada akan diklasifikasikan menjadi 2 (dua) kelas positif dan negatif. Hasil pengujian dengan 500 data dari tweet aplikasi Kredivo menunjukkan bahwa pada pengujian 2 kelas (negatif dan positif) didapatkan hasil terbaik pada 90% data latih dan 10% data uji dengan nilai akurasi sebesar 91,12%.

Pengembangan Sistem Monitoring Kesehatan Berdasarkan Detak Jantung dan Suhu Tubuh Berbasis Internet of Things dengan Metode Fuzzy Mamdani

2024-08-01T01:53:00+00:00

The progress of innovation and data is currently very fast, one sign of which is the presence of the Internet of Things (IoT). IoT is an internet service that is integrated by utilizing certain types of sensorsIn the health sector, both the patient treatment process and the development of health science technology itself is experiencing rapid technological advances. The purpose of this research is to build a health monitoring system based on heart rate and body temperature based on the Internet of Things. The method used in this study is Prototyping.Prototyping is a software development method, which is in the form of a physical working model of the system and functions as an initial version of the system. The prototype of this tool is used to monitor the health of the human body through heart rate and body temperature in humans, namely by using a Pulse sensor and DS18B20 temperature sensor in hand then the results of the sensor readings will appear on the LCD and then configured on the Blynk application on the Smartphone . Based on the results of analysis and testing of health monitoring tools based on heart rate and body temperature based on the Internet of Things. Development of a health monitoring system based on heart rate and body temperature based on the Internet of Things has been made with various electronic components, namely NodeMCU ESP8266, Pulse sensor, DS18B20 temperature sensor , and also a 2x16 LCD. Testing the pulse sensor and temperature sensor DS18B20 is able to detect heart rate and body temperature by holding the two sensors

ALAT KONTROL PERALATAN LISTRIK JARAK JAUH MENGGUNAKAN BLUETOOTH BERBASIS ARDUINO NANO 3.0

2024-08-02T03:42:41+00:00

The use of electrical energy is currently less effective because many electronic equipment consumes electricity excessively. Almost all electronic devices use electricity to work. However, the situation that generally occurs is when someone uses electrical energy, it is often found that electrical energy used to turn on electronic devices is wasted due to the negligence of users who forget to turn off the device and still turn on even though it is not used. To achieve the expected goals, this study uses four stages in its development, namely: Analysis, Design, Implementation, and Testing, which can later support perfection in this research. Connectivity Not affected by weather, and very fast responsibility under 1 second at optimal distances. Testing on the Bluetooth module produces data that Bluetooth can run well as long as it is still within optimal range. System Testing produces data that the development of the Prototype with its various tests has been valid and runs according to what has been expected. In order for this prototype to function further, a device is needed to strengthen the Bluetooth signal. Make the application simpler to make it easier to use without unnecessary features, so the application will be lighter and more responsive. Provide alternative switches, so that if at any time the prototype requires maintenance will not hinder user activities.

Implementasi Algoritma Naive Bayes Dengan Feature Selection Backward Elimination Dalam Pengklasifikasian Status Penderita Stunting Pada Balita

2024-07-19T14:23:43+00:00

Stunting or stunting is one of the nutritional problems experienced by toddlers, where toddlers experience failure to thrive as a result of chronic malnutrition so that toddlers are too short for their age. Broadly speaking, stunting is caused by a lack of nutrition for a long time and the occurrence of recurrent infections, and these two causative factors are influenced by inadequate parenting from the womb to the first 1,000 days of birth. The Asian Development Bank (ADB) reports that the prevalence of children with stunting under the age of five in Indonesia is the second highest in Southeast Asia. Its prevalence reaches 31.8% in 2020. Further monitoring and data collection by the Singgahan Pukesmas regarding stunting cases determines the growth and development factors of toddlers both in the womb and toddlers who have been born. However, the problem that often arises at the Singgahan Pukesmas is that examining the status of stunting in toddlers still takes quite a long time because it is done manually and is also prone to inaccuracies, so a system is needed that can classify toddler examination data to predict whether the child is in stunting or not stunting status. fast and accurate. From the results of this study it can be concluded that the Naive Bayes Algorithm with backward elimination feature selection makes it easier to determine the status of stunted or not stunted toddlers with the variables gender, age, weight, height, BB/U, Z-core BB/U, BB/ TB, Z-Core BB/TB, Z-core TB/U with a total of 450 dataset records, 360 training data records and 90 testing data records taken randomly with an accuracy of 86.11%