This study develops the LSTM modeling to predict time series data of air quality in the city of Bandung from the parameters PM10, ISPU, temperature, and humidity. Modeling LSTM with 4 hidden layers, the number of batch sizes that is 32, the optimizer is adam, value of epoch is 1000, and the loss function using Mean Squared Error. The LSTM modeling results show that the network has a fairly good performance in predicting training and testing. The modeling produces a fairly good prediction accuracy for 3 parameters (temperature, humidity, ISPU). This is indicated by the predicted RMSE value which is smaller than the standard deviation value of the dataset test. However, the prediction results generated from the four test parameters based on the order of the best are the prediction of humidity, temperature, ISPU, and PM10 with unfavorable predictive results. Penelitian ini mengembangkan pemodelan LSTM untuk memprediksi data time series, yaitu kualitas udara di kota Bandung, dari parameter PM10, ISPU, suhu, dan kelembaban. Pemodelan LSTM dengan 4 hidden layer, penentuan jumlah batch size yaitu 32, penentuan optimizer adalah adam, epoch senilai 1000, dan penentuan fungsi loss menggunakan mean squared error. Hasil pemodelan LSTM menunjukkan bahwa jaringan memiliki kinerja yang cukup baik pada prediksi training dan testing. Pemodelan menghasilkan keakuratan prediksi yang cukup baik untuk 3 parameter (suhu, kelembaban, ISPU).Hal ini ditunjukkan dengan nilai RMSE prediksi yang lebih kecil dari nilai standar deviasi uji dataset. Namun, hasil prediksi yang dihasilkan dari keempat parameter pengujian berdasarkan urutan dari yang paling baik yaitu prediksi kelembaban, suhu, ISPU, dan PM10 dengan hasil prediksi yang kurang baik.

The production of coffee plantations has become the leading plantation commodity with the export value of the fourth rank after oil palm, rubber and coconut. The number of coffee needs for export every year always increases, therefore it is necessary to predict the yield of coffee plants to estimate planting and anticipation that will be done so as to achieve the target. Coffee plant productivity is influenced by internal and external factors, namely the quality of the plant itself, soil, altitude and climate. The method used in this study is the CRISP-DM method and multiple linear regression algorithm to predict the amount of coffee production and determine the relationship between the variables. The steps taken are business understanding, data understanding, data preparation, modeling and evaluation. The data set that is used as many as 170 data after going through the data preparation stage produced 150 data with 5 attributes in the table. With calculations using tools, the coefficient of determination is 91.96%. That the variation in the value of the production of coffee plants is influenced by independent variables, namely the area of ​​plantations, rainfall, air pressure and solar radiation by 91.96% and 8.04% influenced by other variables not measured in this study. The results of the evaluation and validation of predictions produce good accuracy with an RMSE value of 0.3477.

Grading is a decisive step in the successful distribution of mangoes to customers according to their preferences for the maturity index. A non-destructive method using near-infrared spectroscopy has historically been used to predict the maturity of fruit. This research classifies the maturity indexes in five classes using a new approach involving classification modeling and the application of fuzzy logic and indirect classification by measuring four parameters: total acidity, soluble solids content, firmness, and starch. These four quantitative parameters provide guidelines for maturity indexes and consumer preferences. The development of portable devices uses a neo spectra micro development kit with specifications for the spectrum of 1350–2500 nm. In terms of computer technology, this study uses a Raspberry Pi and Python programming. To improve the accuracy performance, preprocessing is carried out using 12 spectral transformation operators. Next, these operators are collected and combined to achieve optimal performance. The performance of the classification model with direct and indirect approaches is then compared. Ultimately, classification of the direct approach with preprocessing using linear discriminant analysis offered an accuracy of 91.43%, and classification of the indirect approach using partial least squares with fuzzy logic had an accuracy of 95.7%.

Hama burung pipit merupakan musuh petani dan sulit dikendalikan populasinya. Selama ini pengusirannya menggunakan cara mekanis dengan peralatan tertentu dan tenaga manusia. Pengusiran hama burung pada penelitian sebelumnya untuk deteksi hama menggunakan sensor Passive Infra Red (PIR), sensor ultrasonic, dan Light Dependent Resistor (LDR) serta outputnya berupa gerakan dari motor servo, suara dari kaleng, dan gelombang frekuensi tertentu. Pada penelitian ini menggunakan sensor gerak RCWL Microwave yang memiliki akurasi lebih baik dan outpunya selain gerakan dari motor servo, suara kaleng dari motor DC dan frekuensi dari ultrasonic, juga dilengkapi tembakan laser hijau ke hama burung. Pada penelitian ini menggunakan Arduino Nano ATMega 328, Sensor gerak, Motor Servo, Motor DC, Speaker Ultrasonic, Modul Laser, Power Bank Panel Surya, Modul SIM 800 L. Sistem dapat akses jarak jauh menggunakan aplikasi berbasis desktop dan android. Pengujian yang dihasilkan bahwa sensor gerak dapat mendeteksi dengan baik kumpulan burung, laser hijau masih terlihat pada siang hari, motor servo dapat bergeser hingga 90°, dan speaker ultrasonic berfungsi. Sistem ini akan bekerja optimal jika hama burung dalam bentuk kumpulan dengan lebih dari 5 ekor burung.

The need for fast and accurate information has become the need of every company, including hospitals. This is one of the factors that makes a company superior to other companies. In making the right and accurate decisions, leaders need information that is presented clearly, easily understood, on time, and in accordance with needs. To support the presentation of such information a database, data warehouse and other applications that are easy to understand are needed. Hospitals as a socio-economic institution are not only required to provide solutions to health problems but are also demanded to always improve the quality of their services. For this purpose to be achieved, its management must be efficient. Service indicators can be used as a measuring tool to assess the level of management efficiency. Business Intelligence (BI) application is one form of implementation that is able to facilitate management to monitor hospital performance. This research explores the use of information technology to build BI applications, reviewing the right approach in building BI applications, as well as several important aspects that must be considered for the system work for the hospital environment. There are two main stages in building this application, namely: building a data warehouse originating from an electronic medical record database and hospital operations, and building a BI application. With the formation of this BI application, it is very useful for hospital management in managing their institutions better

Dampak pencemaran air limbah rumah tangga dapat menyebabkan terganggunya kehidupan organisme air karena berkurangnya kandungan oksigen, terjadinya ledakan populasi ganggang dan tumbuhan air, pendangkalan dasar perairan, punahnya biota air, banjir serta menjalarnya wabah penyakit. Adapun penyakit yang ditularkan melaui air dikelompokkan 4 kategori yaitu water borne diseases, water washed diseases, water based diseases, dan water related insect vector diseases. Kondisi sosial dan pengetahuan yang cukup rendah dari masyarakat Dusun Rumambe II Desa Anggadita Karawang dapat memicu dampak pencemaran air. Salah satu usaha untuk mengatasi permasalahan tersebut adalah dengan melaksanakan sosialisasi penanganan air limbah rumah tangga dengan memberikan pengetahuan kesehatan lingkungan, air bersih, sanitasi lingkungan, dan sistem pengolahan air limbah rumah tangga. Bentuk sosialisasi tersebut berupa penyuluhan dan diskusi. Sosialiasasi tentang air limbah, air bersih, masalah dan indikator kesehatan lingkungan, contoh sanitasi lingkungan yang baik dan buruk, penyakit yang dapat ditularkan melalui air, sistem pengolahan air limbah rumah tangga, dan pencemaran lingkungan kepada masyarakat dusun Rumambe II diharapkan mampu mengubah kebiasaan yang salah sehingga mampu menciptakan lingkungan yang sehat dan nyaman.