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IMPLEMENTASI ALGORITMA K-MEANS DAN ALGORITMA APRIORI OPTIMASI KINERJA ECU (STUDY KASUS MOBIL AVANZA DAN XENIA) Sigit Mintoro; Asep Afandi
Jurnal informasi dan komputer Vol 9 No 2 (2021): Jurnal Sistem Informasi dan Komputer yang terbit pada tahun 2021 pada bulan 10 (O
Publisher : STMIK Dian Cipta Cendikia Kotabumi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35959/jik.v9i2.235

Saat ini sistem kendaran sudah dikontrol menggunakan elektronik ECU (Engine Control Unit) .Kerusakan ECU akan mempengaruhi kinerja mesin, maka dibutuhkan sistem yang dapat menangani permasalahan dalam medeteksi secara akurat cepat dalam mengambil keputusan. Dalam clustering data, terdapat beberapa algoritma yang dapat digunakan, seperti, Algoritma K-Means dan Algoritma A Priori adalah algoritma dengan tingkat akurasi yang tinggi dan terbaik di antara ketiga algoritma ini dengan cara melakukan perbandingan menggunakan Rapidminer. Perbandingan algoritma bertujuan untuk mendapatkan hasil dan prediksi dari penelitian yang telah dilakukan. Pengembangan Sistem Analisis dengan K-Mains dan Data Clustering ini menjadi solusi untuk membantu menganalisis data-data dalam proses menganalisa optimasi kinerja ECU terhadap kinerja mesin kendaraan meliputi pengambilan data, mengolah data, medeteksi kelemahan dalam perubahan data digital agar dengan cepat dapat mengoptimalkan kinerja ECU dalam pengelompokan data menggunakan K-means clustering. Dari Hasil penelitian Clustering K-Means didapat C1(781-784), C2(896-927), C3(1223-1321), C4(1460-1587), dan C5(1689-2716) Engine RPM dan A-Priori Suport AUB rata- rata 20% , Suport A rata-rata 80% dan nilai Confidence Rata-Rata 80%. Berdasarkan Pada remapping variasi 3 derajat pengapian maju menghasilkan Daya mesin dan torsi mesi stabil pada putaran rendah 1000 rpm ke putaran tinggi 2176 rpm dengan remapping sesuai dengan kondisi mesin pada saat pengujian.

Implementasi Metode Simple Additive Weighting Dalam Penentuan Bantuan Dana Covid . Dwi marisa Efendi; Asep Afandi; Ferly Ardhy
Prosiding SISFOTEK Vol 4 No 1 (2020): Vol 4 No 1 (2020): SISFOTEK 2020
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (326.506 KB)

Covid pandemic is happening at Indonesian, expecially at lampung.it makes regional goverments needs a system Decision Making System used to determination covid help. Method used is saw. This method uses multiple criteria.Criterias are profesion, land living, electricity, water, fuel, income, floor type, building area. SPK is used to avoid subjectively granting assistance.error value is 0,009043027

Implementasi Sistem Pakar Metode Forward Chaining dan Certainty Factor pada Ayam Pedaging Asep Afandi; Dwi Marisa Efendi
Prosiding SISFOTEK Vol 4 No 1 (2020): Vol 4 No 1 (2020): SISFOTEK 2020
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (648.689 KB)

Disease attacks the farming, it will reduce the poultry productivity. When the chicken get sick, doctors are expected to be able to help treat and prevent diseases to avoid becoming epidemic. However, this does not really help because it takes a long time to call a doctor while the disease keeps spreading. This study aims to build an expert system by applying the Forward Chaining method that has an accuracy calculation to identify broiler diseases and provide a way to overcome it. Expert system development is carried out using the waterfall system development method with the software life cycle approach sequentially including the analysis, design, programming, and testing. The authors used data from PT. Ciomas Adisatwa, a branch of Bandar jaya - Lampung Tengah. The data were directly obtained from Field Extension Officers, through interviews and direct observation. Accuracy measurements were performed using the Certainty Factor methods. Expert System Identification of Broiler Diseases was tested using the Equivalence Class Partitioning method which is a part of Black Box Testing. The tests show Broiler Disease Identification System went well. The Certainty Factor method shows an accuracy of at least 96% for all types of diseases.

Naive Bayes Method and C4.5 in Classification of Birth Data Asep Afandi; Noviana Noviana; Deti Nurdianah
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 16, No 4 (2022): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.78198

Data on the birth and productive age of a mother to get pregnant in Lampung is still high. to find out the comparison of the productive age of pregnant women and whether they have met the minimum and maximum requirements for a mother to become pregnant, and the criteria for babies born. Where the results of data processing will be used as a source of data for counseling mothers, especially for residents of Banjar Kertahayu village. The data processing requires a special method so that the results become a benchmark for a decision later, such as Data Mining. The method used for data processing used is Naive Bayes and C4.5 Algorithm. The data used is birth data in 2017-2021, the source of data from the Banjar Village Midwife-Central Lampung Regency. Research Results Method C 4.5 Middle age has a dominant age category value of 0.3324138. where the highest value is in 2017, and accuracy is 100 percent from the 2017-2021 data. The baby weight criterion using the Naïve Bayes Class Method has a dominant Middle-aged category value of 0.09675, the highest value in 2017, The results of accuracy for 5 years have accuracy of 92.84% based on 2017-2021 birth data

ALGORITMA C4.5 UNTUK MEMPREDIKSI KELAYAKAN PENERIMA BANTUAN PANGAN NON TUNAI Rizal Abi Islahudin; Sidik Rahmatullah; Asep Afandi; Sriyani Safitri
Jurnal Informatika Vol 22, No 2 (2022): Jurnal Informatika
Publisher : IIB Darmajaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30873/ji.v22i2.3367

Pemerintah telah menyiapkan program Bantuan Pangan Non Tunai (BPNT) untuk membantu masyarakat miskin dan membutuhkan. Bantuan Pangan Non Tunai (BPNT) harus disalurkan secara tepat, teratur, dan transparan untuk memastikan bahwa penerima bantuan memang benar-benar mereka yang membutuhkan. Oleh karena itu, diperlukan suatu sistem yang dapat mengubah data menjadi informasi dan mengidentifikasi calon penerima bantuan sembako nontunai maupun yang tidak berhak (BPNT). Sistem prediksi yang akan dibuat pada proyek ini menggunakan RapidMiner 7.1 untuk pengujian dan Algoritma C4.5, metode klasifikasi dari data mining. Hasil Implementasi Data Mining dengan metode Algoritma C4.5 untuk memprediksi kelayakan penerima dan hasil penerima bantuan pangan nontunai (BPNT) diperoleh nilai akurasi prediksi sebesar 99%, yang kemudian divalidasi oleh aplikasi RapidMiner 7.1 dengan akurasi hasil 98,50%.

THE COMPARISON USING EXPECTATION-MAXIMIZATION ALGORITHM AND C4.5 ALGORITHM TO PREDICT THE RESULT OF BIOGAS PRODUCTION AS A POWER PLANT AT PT BUDI STARCH & SWEETENER (BSSW) Eriska Vivian Astuti eriska; Nurmayanti Nurmayanti; Rima Mawarni; Asep Afandi; Aris Munandar
IJISCS (International Journal of Information System and Computer Science) Vol 6, No 3 (2022): IJISCS (International Journal of Information System and Computer Science)
Publisher : STMIK Pringsewu Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/ijiscs.v6i3.1323

Biogas is the result of the development of alternative energy that has formed through the decomposition of organic matter through an anaerobic fermentation process (without oxygen) that produces gas in the form of methane gas (CH4) which has burned. Biogas is a kind of renewable energy because it has a high methane content and calorific value. Methane has one carbon in each chain, which can produce combustion that is more environmentally friendly when compared to fuels that have long carbon chains using specific calculation techniques or methods, a data mining process has been carried out to locate interesting patterns or information in selected data to manipulate the data into more valuable information by extracting significant patterns from the database.

EXPECTATION MAXIMIZATION ALGORITHM MEMPREDIKSI PENJUALAN SUSU MURNI PADA PT. SEWU PRIMATAMA INDONESIA LAMPUNG TENGAH Waspah, Aik Isnayah; Afandi, Asep; Efendi, Dwi Marisa; Sartika, Dwi
JUTIM (Jurnal Teknik Informatika Musirawas) Vol 7 No 1 (2022): JUTIM (Jurnal Teknik Informatika Musirawas) JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jutim.v7i1.1615

Pure milk is a liquid that comes from the udder of healthy and clean cows obtained by proper milking, its natural content is not reduced or added by anything and has not received any treatment. This study focuses on selling whole milk based on the effect of promotional prices as a sales strategy. The purpose of this research is to include data mining steps so that maximum data on pure milk sales can be obtained based on the effect of promotional prices and data reduction using the expectation maximization algorithm and testing using RapidMiner 8.0. The results of the implementation of Data Mining using the expectation maximization algorithm, which are based on sales data and cut price promotional price data are in 2019-2021 the maximum promotional price (MAXx) obtained is 0.22 (22,000) and the minimum value (MINx) is 0, 15 (15000). For pure milk sales in 2019 the maximum (MAXy) sales were 0.3641 (3,641 liters) in November and the minimum value (MINy) was 0.119 (1,190), in 2020 the maximum (MAXy) sales was 0.3861 (3,861 liters) in July, and the minimum value (MINy) is 0.1925 (1.925) and in 2021 the maximum (MAXy) sales is 0.3921 (3,921 liters) in September, and the minimum value (MINy) is 0.2004 (2004) liter.

Forecasting Pertalite Stock Expenditures Using Exponential Smoothing and Linear Regression afandi, asep afandi
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 18, No 4 (2024): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.98849

In the current industrial and business era, effective inventory management is essential for maintaining operational sustainability, particularly in the fuel industry. Pertalite, a popular fuel in Indonesia, with an octane number of 90, offers cleanliness, efficiency, and affordability. However, challenges arise in stock expenditure management due to inaccurate forecasting methods. Data mining, utilizing statistical and machine learning techniques, can identify patterns and trends for better stock forecasting. Recent studies highlight the effectiveness of exponential smoothing and linear regression in fuel demand forecasting. Exponential smoothing, which gives more weight to recent data, improves prediction accuracy, while linear regression analyzes the relationship between fuel stock and various independent variables. This study examines Pertalite fuel sales data from May 2022 to April 2024 from a Pertamina gas station in North Lampung. Results show that linear regression can predict trends, while exponential smoothing, using alpha values between 0.1 and 0.9, captures trends and variations over time. Both methods provide stable forecasts for specific months, demonstrating their utility in understanding Pertalite fuel sales patterns. The study underscores the importance of accurate forecasting in inventory management to meet market demands and maintain operational efficiency.

PELATIHAN PENERAPAN TEKNOLOGI IOT PENYIRAM TANAMAN DI SMKN 4 KOTA SERANG Hidayatullah, M. Syarif; Suhendi, Agus; Aldiansyah, Muhammad; Afandi, Asep
Jurnal Gembira: Pengabdian Kepada Masyarakat Vol 2 No 05 (2024): OKTOBER 2024
Publisher : Media Inovasi Pendidikan dan Publikasi

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

Tujuan dari pelatihan penerapan teknologi Internet of Things (IoT) Penyiram Tanaman yang diadakan di SMKN 4 Kota Serang adalah untuk memperkenalkan siswa kelas XI Dan XII dengan konsep dan aplikasi teknologi Internet of Things (IoT). Diharapkan bahwa kegiatan ini akan membantu siswa memahami konsep dasar IoT dan bagaimana membuat sistem penyiraman tanaman otomatis dengan menggunakan mikrokontroler NodeMCU ESP32 dan sensor kelembaban tanah. Siswa dilatih melalui ceramah, demonstrasi praktis, dan sesi tanya jawab selama satu jam. Hasil dari kegiatan ini menunjukkan bahwa siswa lebih memahami teknologi Internet of Things dan tahu bagaimana membuat sistem penyiraman otomatis yang efektif. Siswa diharapkan dapat menggunakan teknologi Internet of Things dalam kehidupan sehari-hari, khususnya dalam bidang pertanian, untuk meningkatkan efisiensi dan keberlanjutan.

THE COMPARISON USING EXPECTATION-MAXIMIZATION ALGORITHM AND C4.5 ALGORITHM TO PREDICT THE RESULT OF BIOGAS PRODUCTION AS A POWER PLANT AT PT BUDI STARCH & SWEETENER (BSSW) Eriska Vivian Astuti eriska; Nurmayanti Nurmayanti; Rima Mawarni; Asep Afandi; Aris Munandar
IJISCS (International Journal of Information System and Computer Science) Vol 6, No 3 (2022): IJISCS (International Journal of Information System and Computer Science)
Publisher : Bakti Nusantara Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/ijiscs.v6i3.1323

Biogas is the result of the development of alternative energy that has formed through the decomposition of organic matter through an anaerobic fermentation process (without oxygen) that produces gas in the form of methane gas (CH4) which has burned. Biogas is a kind of renewable energy because it has a high methane content and calorific value. Methane has one carbon in each chain, which can produce combustion that is more environmentally friendly when compared to fuels that have long carbon chains using specific calculation techniques or methods, a data mining process has been carried out to locate interesting patterns or information in selected data to manipulate the data into more valuable information by extracting significant patterns from the database.

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