Judul: Pollution Load Analysis of  Cikeas River West Java Province
Abstrak: Sungai Cikeas berperan penting sebagai sumber air tawar. Seiring perkembangan wilayah Sungai Cikeas mengalami perubahan tutupan lahan yang berdampak pada pencemaran Sungai Cikeas. Penelitian ini bertujuan untuk memberikan gambaran kondisi terbaru kualitas air Sungai Cikeas dan besarnya beban pencemaran dari berbagai sumber pencemar pada berbagai tutupan lahan di Sungai Cikeas. Analisis tutupan lahan menggunakan metode OBIA. Status mutu air dianalisis menggunakan metode indeks pencemaran dan baku mutu kelas II. Besarnya beban pencemaran dihitung menggunakan metode rapid assessment. Tutupan lahan DAS Cikeas mengalami perubahan dari lahan pertanian dan hutan menjadi lahan terbangun. Pada periode 2018-2021 Sungai Cikeas berada dalam status tercemar yang berfluktuasi dari cemar berat, sedang, dan ringan. Potensi beban pencemaran terbesar berasal dari sektor industri dan domestik dengan BOD sebesar 18.961,91 ton/tahun, COD sebesar 47.265,24 ton/tahun, dan TSS sebesar 23.182,62 ton/tahun. Pengaruh beban pencemaran terhadap kualitas air, sesuai dinamika debit Sungai Cikeas, menyebabkan peningkatan parameter BOD 3,09- 12,78 mg/l, COD 7,70-31,51 mg/l, dan TSS 3,78-15,46 mg/l.
Keyword: Cikeas River, land cover change, pollution load

Judul: Penetapan Indeks Mutu Air Sungai Cikeas, Sungai Cileungsi, dan Kali Bekasi Berkaitan Tata Guna Lahan
Abstrak: Penggunaan lahan pada daerah aliran Sungai Cileungsi, Cikeas, dan Kali Bekasi yang sangat beragam dapat memberikan pengaruh terhadap kualitas air di ketiga perairan tersebut. Penelitian ini bertujuan untuk menilai status mutu kualitas air sungai Cileungsi, Cikeas, dan Kali Bekasi dalam kaitannya dengan penggunaan lahan. Penelitian dilakukan di Sungai Cileungsi, Sungai Cikeas, dan Kali Bekasi dengan 11 titik lokasi pengamatan. Data spasial yang digunakan dalam penentuan tata guna lahan beserta luasnya berasal dari pengolahan citra satelit dan DEM (digital elevation model). Status kualitas air Sungai Cikeas, Cileungsi, dan Kali Bekasi dalam kategori kelas II berdasarkan metode Indeks Pencemaran dikategorikan tercemar ringan. Hasil analisis menunjukkan luasan tata guna lahan tidak berhubungan secara signifikan dengan parameter DO, BOD, COD, dan amonia kecuali parameter TSS. Kualitas air Sungai Cikeas, Sungai Cileungsi dan Kali Bekasi cenderung menurun ke arah hilir terutama pada segmen sungai yang didominasi lahan terbangun., Land use patterns in the watershed of Cileungsi, Cikeas, and Kali Bekasi Rivers might have deleterious impacts on the water quality of those rivers. In correlation to the land utilization, a study was conducted for the purpose to evaluating water quality status of Cileungsi, Cikeas, and Kali Bekasi Rivers. The study was carried out by sampling of waters at the aforementioned rivers at 11 sampling points. The spatial data used for land use patterns determination and their areas were processed from satellite image data and DEM (Digital Elevation Model). Using Pollution Index Method, the water quality status of Cileungsi, Cikeas and Kali Bekasi Rivers were classified as Category II indicating that the rivers were slightly polluted. Except for TSS, results of the analyses demonstrated that levels of DO, BOD, COD, and ammonia were not significantly correlated to land use patterns. From upstream to downstream, the water quality of Cileungsi, Cikeas, and Kali Bekasi Rivers were decreasing particularly at the river segment dominated by built-up land.
Keyword: land use, pollution index, water quality

Judul: Application of Mel Frequency Cepstrum Coefficients (MFCC) as Feature Extraction on Phoneme Recognition with Probabilistic Neural Network (PNN) as Classifier
Abstrak: Voice recognition (speech recognition) is one field of study in voice processing. This technology can convert voice signals into a form of written information (text). With this technology, people can interact with a computer. MFCC feature extraction computes the cepstral coefficients by considering the human hearing. This research is a phoneme recognition using feature extraction MFCC and PNN as feature matching model. This study compares the coefficient, overlap, and the test data without noise and with noise. MFCC is used with 13, 20, 26 coefficients and 0%, 25%, 50% overlap. Noise is added by 30 dB, 20 dB and 10 dB. From the comparison of the three overlaps, produced the best accuracy at 50% overlap with an accuracy of 94,71%. From comparing the three coefficients, better accuracy resulting in coefficients of 20 and 26 with an accuracy of 97,12% at 50% overlap. After comparing between the coefficients of 20 and 26 with overlap of 25% then the coefficient 26 is obtained that better accuracy of 94,23%. This shows that in this study the coefficient 26 is the best. In this research, there are three noise variables. The variables are 10 dB, 20 dB, and 30 dB. The best accuracy reached when the noise variable is 30 dB rather than 10 dB or 20 dB because the accuracy has the closest accuracy compared by the accuracy when noise was not added into the data. When the noise variable is 30 dB, the percentage of accuracy is 85,3%.
Keyword: